diff --git a/labworks/LW2/data.txt b/labworks/LW2/data.txt
new file mode 100644
index 0000000..7983294
--- /dev/null
+++ b/labworks/LW2/data.txt
@@ -0,0 +1,1000 @@
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diff --git a/labworks/LW2/data_test.txt b/labworks/LW2/data_test.txt
new file mode 100644
index 0000000..34534c9
--- /dev/null
+++ b/labworks/LW2/data_test.txt
@@ -0,0 +1,6 @@
+5.528552372507079760e+00 5.559228990189748920e+00
+5.648388730839360328e+00 5.694735265167601135e+00
+6.034000861003956828e+00 6.014071576183726897e+00
+6.514146742942168800e+00 6.578190189984116643e+00
+6.639221608726088597e+00 6.634545308633034288e+00
+
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diff --git a/labworks/LW2/images/picture2_3.png b/labworks/LW2/images/picture2_3.png
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diff --git a/labworks/LW2/lab02_lib.py b/labworks/LW2/lab02_lib.py
index ec90383..a0888ef 100644
--- a/labworks/LW2/lab02_lib.py
+++ b/labworks/LW2/lab02_lib.py
@@ -29,12 +29,14 @@ from pandas import DataFrame
 from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Activation
+from tensorflow.keras.callbacks import Callback
 
 
 
 visual = True
 verbose_show = False
 
+
 # generate 2d classification dataset
 def datagen(x_c, y_c, n_samples, n_features):
 
@@ -91,8 +93,27 @@ class EarlyStoppingOnValue(tensorflow.keras.callbacks.Callback):
             )
         return monitor_value
 
+
+class VerboseEveryNEpochs(Callback):
+    def __init__(self, every_n_epochs=1000, verbose=1):
+        super().__init__()
+        self.every_n_epochs = every_n_epochs
+        self.verbose = verbose
+
+    def on_epoch_end(self, epoch, logs=None):
+        if (epoch + 1) % self.every_n_epochs == 0:
+            if self.verbose:
+                print(f"\nEpoch {epoch + 1}/{self.params['epochs']}")
+                if logs:
+                    log_str = ", ".join([f"{k}: {v:.4f}" for k, v in logs.items()])
+                    print(f" - {log_str}")
+
+
 #создание и обучение модели автокодировщика
-def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience):
+def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience, **kwargs):
+    verbose_every_n_epochs = kwargs.get('verbose_every_n_epochs', 1000)
+    early_stopping_delta = kwargs.get('early_stopping_delta', 0.001)
+    early_stopping_value = kwargs.get('early_stopping_value', 0.0001)
 
     size = cl_train.shape[1]
     #ans = '2'
@@ -140,22 +161,28 @@ def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience
 
     optimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
     ae.compile(loss='mean_squared_error', optimizer=optimizer)
-    error_stop = 0.0001
     epo = epohs
 
-    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=error_stop)
-    early_stopping_callback_on_improving = tensorflow.keras.callbacks.EarlyStopping(monitor='loss',
-                                                                                           min_delta=0.0001, patience = patience,
-                                                                                           verbose=1, mode='auto',
-                                                                                           baseline=None,
-                                                                                           restore_best_weights=False)
-    history_callback = tensorflow.keras.callbacks.History()
+
     verbose = 1 if verbose_show else 0
+
+    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=early_stopping_value)
+    early_stopping_callback_on_improving = tensorflow.keras.callbacks.EarlyStopping(monitor='loss',
+                                                                                           min_delta=early_stopping_delta, patience = patience,
+                                                                                           verbose=verbose, mode='min',
+                                                                                           baseline=None,
+                                                                                           restore_best_weights=True)
+    history_callback = tensorflow.keras.callbacks.History()
+
     history_object = ae.fit(cl_train, cl_train,
                                 batch_size=cl_train.shape[0],
                                 epochs=epo,
-                                callbacks=[early_stopping_callback_on_error, history_callback,
-                                early_stopping_callback_on_improving],
+                                callbacks=[
+                                  early_stopping_callback_on_error, 
+                                  history_callback,
+                                  early_stopping_callback_on_improving,
+                                  VerboseEveryNEpochs(every_n_epochs=verbose_every_n_epochs),
+                                ],
                                 verbose=verbose)
     ae_trainned = ae
     ae_pred = ae_trainned.predict(cl_train)
@@ -538,4 +565,4 @@ def ire_plot(title, IRE_test, IREth, ae_name):
     plt.gcf().savefig('out/IRE_' + title + ae_name + '.png')
     plt.show()
 
-    return
\ No newline at end of file
+    return
diff --git a/labworks/LW2/lab2.ipynb b/labworks/LW2/lab2.ipynb
new file mode 100644
index 0000000..d763395
--- /dev/null
+++ b/labworks/LW2/lab2.ipynb
@@ -0,0 +1,871 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "1c1905f6-acba-4dba-aba9-4d2fe59298df",
+      "metadata": {
+        "id": "1c1905f6-acba-4dba-aba9-4d2fe59298df"
+      },
+      "source": [
+        "## Задание 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "61f467d3-d1c1-481a-8560-f259a22e0824",
+      "metadata": {
+        "id": "61f467d3-d1c1-481a-8560-f259a22e0824"
+      },
+      "source": [
+        "### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "58e787ca-562f-458d-8c62-e86b6fb1580a",
+      "metadata": {
+        "id": "58e787ca-562f-458d-8c62-e86b6fb1580a"
+      },
+      "outputs": [],
+      "source": [
+        "# импорт модулей\n",
+        "import os\n",
+        "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')\n",
+        "\n",
+        "import numpy as np\n",
+        "import lab02_lib as lib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5bbb8290-8806-4184-b33b-f1b0680bf563",
+      "metadata": {
+        "id": "5bbb8290-8806-4184-b33b-f1b0680bf563"
+      },
+      "source": [
+        "### 2) Сгенерировали индивидуальный набор двумерных данных в пространстве признаков с координатами центра (6, 6), где 6 – номер бригады. Вывели полученные данные на рисунок и в консоль."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "29603d03-8478-4341-b7ad-bb608ad88890",
+      "metadata": {
+        "id": "29603d03-8478-4341-b7ad-bb608ad88890"
+      },
+      "outputs": [],
+      "source": [
+        "# генерация датасета\n",
+        "data = lib.datagen(6, 6, 1000, 2)\n",
+        "\n",
+        "# вывод данных и размерности\n",
+        "print('Исходные данные:')\n",
+        "print(data)\n",
+        "print('Размерность данных:')\n",
+        "print(data.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "00ba79e5-d8f9-4c40-a065-e8fd21a68e21",
+      "metadata": {
+        "id": "00ba79e5-d8f9-4c40-a065-e8fd21a68e21"
+      },
+      "source": [
+        "### 3) Создали и обучили автокодировщик AE1 простой архитектуры, выбрав небольшое количество эпох обучения. Зафиксировали в таблице вида табл.1 количество скрытых слоёв и нейронов в них"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bc736f50-204b-4b1d-8c1c-0dd34fb1f02b",
+      "metadata": {
+        "id": "bc736f50-204b-4b1d-8c1c-0dd34fb1f02b"
+      },
+      "outputs": [],
+      "source": [
+        "# обучение AE1\n",
+        "patience = 300\n",
+        "ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt',\n",
+        "1000, True, patience)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ae9e6816-d300-455a-a4ee-77e4911e02f5",
+      "metadata": {
+        "id": "ae9e6816-d300-455a-a4ee-77e4911e02f5"
+      },
+      "source": [
+        "### 4) Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ошибка MSE_AE1 = 19.5568"
+      ],
+      "metadata": {
+        "id": "Np8zquNSgtO9"
+      },
+      "id": "Np8zquNSgtO9"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fb7eeae8-f478-4295-b6de-b5790b7861f2",
+      "metadata": {
+        "id": "fb7eeae8-f478-4295-b6de-b5790b7861f2"
+      },
+      "outputs": [],
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE1, IREth1, 'AE1')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "92fb420f-907e-4417-ab34-6d12308bed0b",
+      "metadata": {
+        "id": "92fb420f-907e-4417-ab34-6d12308bed0b"
+      },
+      "source": [
+        "### 5) Создали и обучили второй автокодировщик AE2 с усложненной архитектурой, задав большее количество эпох обучения"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bb9f83e2-fecf-4118-8376-a712726d12d5",
+      "metadata": {
+        "id": "bb9f83e2-fecf-4118-8376-a712726d12d5"
+      },
+      "outputs": [],
+      "source": [
+        "# обучение AE2\n",
+        "ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt',\n",
+        "3000, True, patience)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "03194c1b-c03e-42a5-93e5-f4d007ddb7c7",
+      "metadata": {
+        "id": "03194c1b-c03e-42a5-93e5-f4d007ddb7c7"
+      },
+      "source": [
+        "### 6) Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали второй порог ошибки реконструкции – порог обнаружения аномалий."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ошибка MSE_AE2 = 0.0108"
+      ],
+      "metadata": {
+        "id": "6TBPoMskhCQp"
+      },
+      "id": "6TBPoMskhCQp"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "47cf147a-f97b-41d3-9d7c-b799da0115fe",
+      "metadata": {
+        "id": "47cf147a-f97b-41d3-9d7c-b799da0115fe"
+      },
+      "outputs": [],
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE2, IREth2, 'AE2')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fd14d331-aad6-4b98-9926-5ab6a055beb0",
+      "metadata": {
+        "id": "fd14d331-aad6-4b98-9926-5ab6a055beb0"
+      },
+      "source": [
+        "### 7) Рассчитали характеристики качества обучения EDCA для AE1 и AE2. Визуализировали и сравнили области пространства признаков, распознаваемые автокодировщиками AE1 и AE2. Сделали вывод о пригодности AE1 и AE2 для качественного обнаружения аномалий.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "58fd0b46-1064-4f2f-b04f-88307bf938d2",
+      "metadata": {
+        "id": "58fd0b46-1064-4f2f-b04f-88307bf938d2"
+      },
+      "outputs": [],
+      "source": [
+        "# построение областей покрытия и границ классов\n",
+        "# расчет характеристик качества обучения\n",
+        "numb_square = 20\n",
+        "xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# построение областей покрытия и границ классов\n",
+        "# расчет характеристик качества обучения\n",
+        "numb_square = 20\n",
+        "xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)"
+      ],
+      "metadata": {
+        "id": "ZG72thVfYMOy"
+      },
+      "id": "ZG72thVfYMOy",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сравнение характеристик качества обучения и областей аппроксимации\n",
+        "lib.plot2in1(data, xx, yy, Z1, Z2)"
+      ],
+      "metadata": {
+        "id": "RJWYYYHQbUIN"
+      },
+      "id": "RJWYYYHQbUIN",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d766b8f0-3ba7-4a73-94a0-7858eb2f5515",
+      "metadata": {
+        "id": "d766b8f0-3ba7-4a73-94a0-7858eb2f5515"
+      },
+      "source": [
+        "### 8) Если автокодировщик AE2 недостаточно точно аппроксимирует область обучающих данных, то подобрать подходящие параметры автокодировщика и повторить шаги (6) – (8)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Полученные показатели EDCA для автокодировщика AE2 нас устраивают."
+      ],
+      "metadata": {
+        "id": "XbnbvTima5dv"
+      },
+      "id": "XbnbvTima5dv"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cfa38b82-4c8e-4437-9c25-581d7fea0f2c",
+      "metadata": {
+        "id": "cfa38b82-4c8e-4437-9c25-581d7fea0f2c"
+      },
+      "source": [
+        "### 9) Изучили сохраненный набор данных и пространство признаков. Создали тестовую выборку, состоящую, как минимум, из 4ёх элементов, не входящих в обучающую выборку. Элементы должны быть такими, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d618382f-f65a-4c17-b0d0-3d2d0c4bc50e",
+      "metadata": {
+        "id": "d618382f-f65a-4c17-b0d0-3d2d0c4bc50e"
+      },
+      "outputs": [],
+      "source": [
+        "# загрузка тестового набора\n",
+        "data_test = np.loadtxt('data_test.txt', dtype=float)\n",
+        "print(data_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 10) Применили обученные автокодировщики AE1 и AE2 к тестовым данным и вывели значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога на график и в консоль."
+      ],
+      "metadata": {
+        "id": "3Ce8wSVMdjBj"
+      },
+      "id": "3Ce8wSVMdjBj"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE1\n",
+        "predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)"
+      ],
+      "metadata": {
+        "id": "Hq8A0LyMcKXk"
+      },
+      "id": "Hq8A0LyMcKXk",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE1\n",
+        "lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)\n",
+        "lib.ire_plot('test', ire1, IREth1, 'AE1')"
+      ],
+      "metadata": {
+        "id": "Z_v2X4HvcQD5"
+      },
+      "id": "Z_v2X4HvcQD5",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)"
+      ],
+      "metadata": {
+        "id": "fT75bpEnc7a2"
+      },
+      "id": "fT75bpEnc7a2",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)\n",
+        "lib.ire_plot('test', ire2, IREth2, 'AE2')"
+      ],
+      "metadata": {
+        "id": "EpGtgYZVdAen"
+      },
+      "id": "EpGtgYZVdAen",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7acf3a7f-4dd8-44df-b201-509aa4caa7f0",
+      "metadata": {
+        "id": "7acf3a7f-4dd8-44df-b201-509aa4caa7f0"
+      },
+      "source": [
+        "### 11) Визуализировали элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a2fbf3a9-5e11-49d0-a0db-f77f3edeb7b7",
+      "metadata": {
+        "id": "a2fbf3a9-5e11-49d0-a0db-f77f3edeb7b7"
+      },
+      "outputs": [],
+      "source": [
+        "# построение областей аппроксимации и точек тестового набора\n",
+        "lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bd63edf4-3452-41db-8080-d8a2ff4f09a4",
+      "metadata": {
+        "id": "bd63edf4-3452-41db-8080-d8a2ff4f09a4"
+      },
+      "source": [
+        "### 12) Результаты исследования занесли в таблицу:\n",
+        "Табл. 1 Результаты задания №1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "|      | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | Значение показателя<br>Excess | Значение показателя<br>Approx | Количество обнаруженных<br>аномалий |\n",
+        "|-----:|------------------------------|----------------------------------------|-----------------------------|--------------------|-------------------------------|-------------------------------|--------------------------------|-------------------------------------|\n",
+        "| AE1  | 1                            | 1                            | 1000                        | 19.5568             | 6.53                         | 12.67                          | 0.073                           | 0                                   |\n",
+        "| AE2  | 5                            | 3,2,1,2,3                                | 3000                          | 0.0108             | 0.4                         | 0.33                          | 0.750                           | 4                                   |\n"
+      ],
+      "metadata": {
+        "id": "df2BIryKfkpk"
+      },
+      "id": "df2BIryKfkpk"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b8e54861-e2c2-4755-a2d8-5d4b6cace481",
+      "metadata": {
+        "id": "b8e54861-e2c2-4755-a2d8-5d4b6cace481"
+      },
+      "source": [
+        "### 13) Сделали выводы о требованиях к:\n",
+        "- данным для обучения,\n",
+        "- архитектуре автокодировщика,\n",
+        "- количеству эпох обучения,\n",
+        "- ошибке MSE_stop, приемлемой для останова обучения,\n",
+        "- ошибке реконструкции обучающей выборки (порогу обнаружения\n",
+        "аномалий),\n",
+        "- характеристикам качества обучения EDCA одноклассового\n",
+        "классификатора\n",
+        "\n",
+        "для качественного обнаружения аномалий в данных."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1) Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение\n",
+        "2) Архитектура автокодировщика должна постепенно сужатся к бутылочному горлышку,а затем постепенно возвращатся к исходным выходным размерам, кол-во скрытых слоев 3-5\n",
+        "3) В рамках данного набора данных оптимальное кол-во эпох 3000 с patience 300 эпох\n",
+        "4) Оптимальная ошибка MSE-stop в районе 0.01, желательно не меньше для предотвращения переобучения\n",
+        "5) Значение порога в районе 0.4\n",
+        "6) Значение Excess не больше 0.5, значение Deficit равное 0, значение Coating равное 1, значение Approx не меньше 0.7"
+      ],
+      "metadata": {
+        "id": "1s5_ye8vkleI"
+      },
+      "id": "1s5_ye8vkleI"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задание 2"
+      ],
+      "metadata": {
+        "id": "rqdVflKXo6Bo"
+      },
+      "id": "rqdVflKXo6Bo"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1) Изучить описание своего набора реальных данных, что он из себя представляет"
+      ],
+      "metadata": {
+        "id": "rSvJtTReo928"
+      },
+      "id": "rSvJtTReo928"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Бригада 6 => набор данных Cardio. Это реальный набор данных, который состоит из измерений частоты сердечных сокращений плода и\n",
+        "сокращений матки на кардиотокограммах, классифицированных экспертами\n",
+        "акушерами. Исходный набор данных предназначен для классификации. В нем\n",
+        "представлено 3 класса: «норма», «подозрение» и «патология». Для обнаружения\n",
+        "аномалий класс «норма» принимается за норму, класс «патология» принимается за\n",
+        "аномалии, а класс «подозрение» был отброшен.\n",
+        "\n",
+        "| Количество<br>признаков | Количество<br>примеров | Количество<br>нормальных примеров | Количество<br>аномальных примеров |\n",
+        "|-------------------------:|-----------------------:|----------------------------------:|-----------------------------------:|\n",
+        "| 21                       | 1764                   | 1655                               | 109                                |\n"
+      ],
+      "metadata": {
+        "id": "gf-0gJ7jqTdk"
+      },
+      "id": "gf-0gJ7jqTdk"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2) Загрузить многомерную обучающую выборку реальных данных Cardio.txt."
+      ],
+      "metadata": {
+        "id": "N2Egw1pho-F_"
+      },
+      "id": "N2Egw1pho-F_"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка обчуающей выборки\n",
+        "train = np.loadtxt('data/cardio_train.txt', dtype=float)"
+      ],
+      "metadata": {
+        "id": "G8QTxAFapASY"
+      },
+      "id": "G8QTxAFapASY",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3) Вывести полученные данные и их размерность в консоли."
+      ],
+      "metadata": {
+        "id": "zj2WXPNco-Tz"
+      },
+      "id": "zj2WXPNco-Tz"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('train:\\n', train)\n",
+        "print('train.shape:', np.shape(train))"
+      ],
+      "metadata": {
+        "id": "W6hTlfk6pAo9"
+      },
+      "id": "W6hTlfk6pAo9",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4) Создать и обучить автокодировщик с подходящей для данных архитектурой. Выбрать необходимое количество эпох обучения."
+      ],
+      "metadata": {
+        "id": "0T11A0x4o-gr"
+      },
+      "id": "0T11A0x4o-gr"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# **kwargs\n",
+        "# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)\n",
+        "# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)\n",
+        "# early_stopping_value = значение для ранней остановки (по умолчанию -  0.0001)\n",
+        "\n",
+        "from time import time\n",
+        "\n",
+        "patience = 4000\n",
+        "start = time()\n",
+        "ae3_v1_trained, IRE3_v1, IREth3_v1 = lib.create_fit_save_ae(train,'out/AE3_V1.h5','out/AE3_v1_ire_th.txt',\n",
+        "100000, False, patience, early_stopping_delta = 0.001)\n",
+        "print(\"Время на обучение: \", time() - start)"
+      ],
+      "metadata": {
+        "id": "rPwtBtdRPztp"
+      },
+      "id": "rPwtBtdRPztp",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5) Зафиксировать ошибку MSE, на которой обучение завершилось. Построить график ошибки реконструкции обучающей выборки. Зафиксировать порог ошибки реконструкции – порог обнаружения аномалий."
+      ],
+      "metadata": {
+        "id": "8ALjaY8lo-sa"
+      },
+      "id": "8ALjaY8lo-sa"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Скрытых слоев 7, нейроны: 46->26->14->10->14->26->48\n",
+        "\n",
+        "Ошибка MSE_AE3_v1 = 0.0126"
+      ],
+      "metadata": {
+        "id": "b4sk_fhYY6Qb"
+      },
+      "id": "b4sk_fhYY6Qb"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE3_v1, IREth3_v1, 'AE3_v1')"
+      ],
+      "metadata": {
+        "id": "a4sR3SSDpBPU"
+      },
+      "id": "a4sR3SSDpBPU",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6) Сделать вывод о пригодности обученного автокодировщика для качественного обнаружения аномалий. Если порог ошибки реконструкции слишком велик, то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (6)."
+      ],
+      "metadata": {
+        "id": "DGBM9xNFo-4k"
+      },
+      "id": "DGBM9xNFo-4k"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# **kwargs\n",
+        "# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)\n",
+        "# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)\n",
+        "# early_stopping_value = значение для ранней остановки (по умолчанию -  0.0001)\n",
+        "\n",
+        "from time import time\n",
+        "\n",
+        "patience = 4000\n",
+        "start = time()\n",
+        "ae3_v2_trained, IRE3_v2, IREth3_v2 = lib.create_fit_save_ae(train,'out/AE3_V2.h5','out/AE3_v2_ire_th.txt',\n",
+        "100000, False, patience, early_stopping_delta = 0.001)\n",
+        "print(\"Время на обучение: \", time() - start)"
+      ],
+      "metadata": {
+        "id": "ZvM1VbKEgalO"
+      },
+      "id": "ZvM1VbKEgalO",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Скрытых слоев 7, нейроны: 48->36->28->22->16->12->16->22->28->36->48\n",
+        "\n",
+        "Ошибка MSE_AE3_v1 = 0.0098"
+      ],
+      "metadata": {
+        "id": "9BrPUb_8fX5R"
+      },
+      "id": "9BrPUb_8fX5R"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE3_v2, IREth3_v2, 'AE3_v2')"
+      ],
+      "metadata": {
+        "id": "kh1eMtvpf6F-"
+      },
+      "id": "kh1eMtvpf6F-",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 7) Изучить и загрузить тестовую выборку Cardio.txt."
+      ],
+      "metadata": {
+        "id": "vJvW1mOtpgFO"
+      },
+      "id": "vJvW1mOtpgFO"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#загрузка тестовой выборки\n",
+        "test = np.loadtxt('data/cardio_test.txt', dtype=float)\n",
+        "print('\\n test:\\n', test)\n",
+        "print('test.shape:', np.shape(test))"
+      ],
+      "metadata": {
+        "id": "GrXQ5YymtD2u"
+      },
+      "id": "GrXQ5YymtD2u",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 8) Подать тестовую выборку на вход обученного автокодировщика для обнаружения аномалий. Вывести график ошибки реконструкции элементов тестовой выборки относительно порога."
+      ],
+      "metadata": {
+        "id": "6iqO4Yxbpgob"
+      },
+      "id": "6iqO4Yxbpgob"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE3\n",
+        "predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1)"
+      ],
+      "metadata": {
+        "id": "Vxj1fTAikDuU"
+      },
+      "id": "Vxj1fTAikDuU",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')"
+      ],
+      "metadata": {
+        "id": "21DdnIKYtmtM"
+      },
+      "id": "21DdnIKYtmtM",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE3\n",
+        "predicted_labels3_v2, ire3_v2 = lib.predict_ae(ae3_v2_trained, test, IREth3_v2)"
+      ],
+      "metadata": {
+        "id": "Ql1JMAM6sf7Z"
+      },
+      "id": "Ql1JMAM6sf7Z",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')"
+      ],
+      "metadata": {
+        "id": "jczBRWjTt35R"
+      },
+      "id": "jczBRWjTt35R",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)"
+      ],
+      "metadata": {
+        "id": "2kF1XknIpg48"
+      },
+      "id": "2kF1XknIpg48",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Для AE3_v1 точность составляет 88%"
+      ],
+      "metadata": {
+        "id": "LXHt7-gxuJIy"
+      },
+      "id": "LXHt7-gxuJIy"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)"
+      ],
+      "metadata": {
+        "id": "5ow1D88nsrir"
+      },
+      "id": "5ow1D88nsrir",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Для AE3_v2 точность составляет 92%"
+      ],
+      "metadata": {
+        "id": "LDsC5WyeuRHS"
+      },
+      "id": "LDsC5WyeuRHS"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 9) Если результаты обнаружения аномалий не удовлетворительные (обнаружено менее 70% аномалий), то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (9)."
+      ],
+      "metadata": {
+        "id": "fPPbYwnQpqS3"
+      },
+      "id": "fPPbYwnQpqS3"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Результаты обнаружения аномалий удовлетворены."
+      ],
+      "metadata": {
+        "id": "IVmw2aeduXml"
+      },
+      "id": "IVmw2aeduXml"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 10) Параметры наилучшего автокодировщика и результаты обнаружения аномалий занести в таблицу:\n",
+        "Табл. 2 Результаты задания №2"
+      ],
+      "metadata": {
+        "id": "u3cAX_IgpvWU"
+      },
+      "id": "u3cAX_IgpvWU"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "| Dataset name | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | % обнаруженных<br>аномалий |\n",
+        "|:-------------|:-----------------------------|:----------------------------------------|:-----------------------------|:-------------------|:-------------------------------|:---------------------------|\n",
+        "| Cardio       | 11                             | 48, 36, 28, 22, 16, 10, 16, 22, 28, 36, 48              | 100000                      | 0.0098              | 1.6                           | 92%                      |\n"
+      ],
+      "metadata": {
+        "id": "tryfJdjTvgU8"
+      },
+      "id": "tryfJdjTvgU8"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 11) Сделать выводы о требованиях к:\n",
+        "- данным для обучения,\n",
+        "- архитектуре автокодировщика,  \n",
+        "- количеству эпох обучения,\n",
+        "- ошибке MSE_stop, приемлемой для останова обучения,\n",
+        "- ошибке реконструкции обучающей выборки (порогу обнаружения\n",
+        "аномалий)\n",
+        "\n",
+        "для качественного обнаружения аномалий в случае, когда размерность\n",
+        "пространства признаков высока."
+      ],
+      "metadata": {
+        "id": "eE7IYyGJp0Vv"
+      },
+      "id": "eE7IYyGJp0Vv"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1) Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение\n",
+        "2) Архитектура автокодировщика должна постепенно сужатся к бутылочному горлышку,а затем постепенно возвращатся к исходным выходным размерам, кол-во скрытых слоев 7-11.\n",
+        "3) В рамках данного набора данных оптимальное кол-во эпох 100000 с patience 4000 эпох\n",
+        "4) Оптимальная ошибка MSE-stop в районе 0.001, желательно не меньше для предотвращения переобучения\n",
+        "5) Значение порога не больше 1.6"
+      ],
+      "metadata": {
+        "id": "67p7AeSWwVXE"
+      },
+      "id": "67p7AeSWwVXE"
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/labworks/LW2/notebook с полными выводами/LR2_задание1.ipynb b/labworks/LW2/notebook с полными выводами/LR2_задание1.ipynb
new file mode 100644
index 0000000..4c62d32
--- /dev/null
+++ b/labworks/LW2/notebook с полными выводами/LR2_задание1.ipynb	
@@ -0,0 +1,7832 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "1c1905f6-acba-4dba-aba9-4d2fe59298df",
+      "metadata": {
+        "id": "1c1905f6-acba-4dba-aba9-4d2fe59298df"
+      },
+      "source": [
+        "## Задание 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "61f467d3-d1c1-481a-8560-f259a22e0824",
+      "metadata": {
+        "id": "61f467d3-d1c1-481a-8560-f259a22e0824"
+      },
+      "source": [
+        "### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "58e787ca-562f-458d-8c62-e86b6fb1580a",
+      "metadata": {
+        "id": "58e787ca-562f-458d-8c62-e86b6fb1580a"
+      },
+      "outputs": [],
+      "source": [
+        "# импорт модулей\n",
+        "import os\n",
+        "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')\n",
+        "\n",
+        "import numpy as np\n",
+        "import lab02_lib as lib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5bbb8290-8806-4184-b33b-f1b0680bf563",
+      "metadata": {
+        "id": "5bbb8290-8806-4184-b33b-f1b0680bf563"
+      },
+      "source": [
+        "### 2) Сгенерировали индивидуальный набор двумерных данных в пространстве признаков с координатами центра (6, 6), где 6 – номер бригады. Вывели полученные данные на рисунок и в консоль."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "29603d03-8478-4341-b7ad-bb608ad88890",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 901
+        },
+        "id": "29603d03-8478-4341-b7ad-bb608ad88890",
+        "outputId": "59d12923-8a3c-4145-d6bc-ea85ff8adef2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Исходные данные:\n",
+            "[[5.8619435  5.99297106]\n",
+            " [6.12066008 5.98527609]\n",
+            " [5.98643623 5.9813867 ]\n",
+            " ...\n",
+            " [5.80640873 6.16429542]\n",
+            " [6.01437944 6.13645626]\n",
+            " [6.00333385 5.99329265]]\n",
+            "Размерность данных:\n",
+            "(1000, 2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# генерация датасета\n",
+        "data = lib.datagen(6, 6, 1000, 2)\n",
+        "\n",
+        "# вывод данных и размерности\n",
+        "print('Исходные данные:')\n",
+        "print(data)\n",
+        "print('Размерность данных:')\n",
+        "print(data.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "00ba79e5-d8f9-4c40-a065-e8fd21a68e21",
+      "metadata": {
+        "id": "00ba79e5-d8f9-4c40-a065-e8fd21a68e21"
+      },
+      "source": [
+        "### 3) Создали и обучили автокодировщик AE1 простой архитектуры, выбрав небольшое количество эпох обучения. Зафиксировали в таблице вида табл.1 количество скрытых слоёв и нейронов в них"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bc736f50-204b-4b1d-8c1c-0dd34fb1f02b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bc736f50-204b-4b1d-8c1c-0dd34fb1f02b",
+        "outputId": "8f082198-c299-4beb-8043-92716c2c0984"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n",
+            "Задайте количество скрытых слоёв (нечетное число) : 1\n",
+            "Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 1\n",
+            "Epoch 1/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - loss: 38.5869\n",
+            "Epoch 2/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 38.5613\n",
+            "Epoch 3/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 38.5357\n",
+            "Epoch 4/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 38.5101\n",
+            "Epoch 5/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 38.4846\n",
+            "Epoch 6/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 38.4590\n",
+            "Epoch 7/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 38.4335\n",
+            "Epoch 8/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 38.4080\n",
+            "Epoch 9/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 38.3825\n",
+            "Epoch 10/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 38.3570\n",
+            "Epoch 11/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 38.3315\n",
+            "Epoch 12/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 38.3060\n",
+            "Epoch 13/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 38.2805\n",
+            "Epoch 14/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 38.2551\n",
+            "Epoch 15/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 38.2297\n",
+            "Epoch 16/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 38.2042\n",
+            "Epoch 17/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 38.1788\n",
+            "Epoch 18/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 38.1535\n",
+            "Epoch 19/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 38.1281\n",
+            "Epoch 20/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 38.1027\n",
+            "Epoch 21/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 38.0774\n",
+            "Epoch 22/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 38.0520\n",
+            "Epoch 23/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 38.0267\n",
+            "Epoch 24/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 38.0014\n",
+            "Epoch 25/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 37.9761\n",
+            "Epoch 26/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 37.9509\n",
+            "Epoch 27/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.9256\n",
+            "Epoch 28/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 37.9004\n",
+            "Epoch 29/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 37.8752\n",
+            "Epoch 30/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.8500\n",
+            "Epoch 31/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.8248\n",
+            "Epoch 32/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 37.7996\n",
+            "Epoch 33/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 37.7745\n",
+            "Epoch 34/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.7493\n",
+            "Epoch 35/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.7242\n",
+            "Epoch 36/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 37.6991\n",
+            "Epoch 37/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.6740\n",
+            "Epoch 38/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.6490\n",
+            "Epoch 39/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 37.6239\n",
+            "Epoch 40/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 37.5989\n",
+            "Epoch 41/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 37.5739\n",
+            "Epoch 42/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 37.5489\n",
+            "Epoch 43/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 37.5239\n",
+            "Epoch 44/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 37.4990\n",
+            "Epoch 45/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 37.4741\n",
+            "Epoch 46/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 37.4492\n",
+            "Epoch 47/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 37.4243\n",
+            "Epoch 48/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 37.3994\n",
+            "Epoch 49/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 37.3745\n",
+            "Epoch 50/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 37.3497\n",
+            "Epoch 51/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 37.3249\n",
+            "Epoch 52/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 37.3001\n",
+            "Epoch 53/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 37.2753\n",
+            "Epoch 54/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 37.2506\n",
+            "Epoch 55/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 37.2258\n",
+            "Epoch 56/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 37.2011\n",
+            "Epoch 57/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 37.1764\n",
+            "Epoch 58/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 37.1518\n",
+            "Epoch 59/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 37.1271\n",
+            "Epoch 60/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 37.1025\n",
+            "Epoch 61/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 37.0779\n",
+            "Epoch 62/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 37.0533\n",
+            "Epoch 63/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 37.0287\n",
+            "Epoch 64/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 37.0042\n",
+            "Epoch 65/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 36.9797\n",
+            "Epoch 66/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 36.9552\n",
+            "Epoch 67/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 36.9307\n",
+            "Epoch 68/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 36.9062\n",
+            "Epoch 69/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 36.8818\n",
+            "Epoch 70/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 36.8574\n",
+            "Epoch 71/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 36.8330\n",
+            "Epoch 72/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 36.8087\n",
+            "Epoch 73/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 36.7843\n",
+            "Epoch 74/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 36.7600\n",
+            "Epoch 75/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 36.7357\n",
+            "Epoch 76/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 36.7115\n",
+            "Epoch 77/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 36.6872\n",
+            "Epoch 78/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 36.6630\n",
+            "Epoch 79/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 36.6388\n",
+            "Epoch 80/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 36.6146\n",
+            "Epoch 81/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 36.5905\n",
+            "Epoch 82/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 36.5664\n",
+            "Epoch 83/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 36.5423\n",
+            "Epoch 84/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 36.5182\n",
+            "Epoch 85/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 36.4941\n",
+            "Epoch 86/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 36.4701\n",
+            "Epoch 87/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 36.4461\n",
+            "Epoch 88/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 36.4221\n",
+            "Epoch 89/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 36.3982\n",
+            "Epoch 90/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 36.3743\n",
+            "Epoch 91/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 36.3504\n",
+            "Epoch 92/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 36.3265\n",
+            "Epoch 93/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 36.3027\n",
+            "Epoch 94/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 36.2788\n",
+            "Epoch 95/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 36.2550\n",
+            "Epoch 96/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 36.2313\n",
+            "Epoch 97/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 36.2075\n",
+            "Epoch 98/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 36.1838\n",
+            "Epoch 99/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 36.1601\n",
+            "Epoch 100/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 36.1365\n",
+            "Epoch 101/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 36.1128\n",
+            "Epoch 102/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 36.0892\n",
+            "Epoch 103/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 36.0657\n",
+            "Epoch 104/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 36.0421\n",
+            "Epoch 105/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 36.0186\n",
+            "Epoch 106/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 35.9951\n",
+            "Epoch 107/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.9716\n",
+            "Epoch 108/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.9482\n",
+            "Epoch 109/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.9247\n",
+            "Epoch 110/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.9013\n",
+            "Epoch 111/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 35.8780\n",
+            "Epoch 112/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 35.8546\n",
+            "Epoch 113/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 35.8313\n",
+            "Epoch 114/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 35.8081\n",
+            "Epoch 115/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 35.7848\n",
+            "Epoch 116/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 35.7616\n",
+            "Epoch 117/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.7384\n",
+            "Epoch 118/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.7152\n",
+            "Epoch 119/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.6921\n",
+            "Epoch 120/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 35.6689\n",
+            "Epoch 121/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 35.6459\n",
+            "Epoch 122/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.6228\n",
+            "Epoch 123/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 35.5997\n",
+            "Epoch 124/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 35.5767\n",
+            "Epoch 125/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.5537\n",
+            "Epoch 126/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 35.5308\n",
+            "Epoch 127/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.5078\n",
+            "Epoch 128/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 35.4849\n",
+            "Epoch 129/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.4620\n",
+            "Epoch 130/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 35.4392\n",
+            "Epoch 131/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 35.4163\n",
+            "Epoch 132/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 35.3935\n",
+            "Epoch 133/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.3707\n",
+            "Epoch 134/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.3479\n",
+            "Epoch 135/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.3252\n",
+            "Epoch 136/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 35.3025\n",
+            "Epoch 137/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.2797\n",
+            "Epoch 138/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.2570\n",
+            "Epoch 139/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 35.2344\n",
+            "Epoch 140/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 35.2117\n",
+            "Epoch 141/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 35.1890\n",
+            "Epoch 142/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 35.1664\n",
+            "Epoch 143/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 35.1438\n",
+            "Epoch 144/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 35.1212\n",
+            "Epoch 145/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.0986\n",
+            "Epoch 146/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 35.0760\n",
+            "Epoch 147/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 35.0534\n",
+            "Epoch 148/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 35.0308\n",
+            "Epoch 149/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 35.0082\n",
+            "Epoch 150/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.9856\n",
+            "Epoch 151/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.9630\n",
+            "Epoch 152/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 34.9404\n",
+            "Epoch 153/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 34.9178\n",
+            "Epoch 154/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 34.8952\n",
+            "Epoch 155/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.8726\n",
+            "Epoch 156/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.8499\n",
+            "Epoch 157/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 34.8272\n",
+            "Epoch 158/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 34.8046\n",
+            "Epoch 159/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.7818\n",
+            "Epoch 160/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 34.7591\n",
+            "Epoch 161/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.7363\n",
+            "Epoch 162/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.7134\n",
+            "Epoch 163/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.6905\n",
+            "Epoch 164/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 34.6676\n",
+            "Epoch 165/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.6446\n",
+            "Epoch 166/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 34.6215\n",
+            "Epoch 167/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 34.5984\n",
+            "Epoch 168/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.5751\n",
+            "Epoch 169/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.5518\n",
+            "Epoch 170/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.5284\n",
+            "Epoch 171/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 34.5049\n",
+            "Epoch 172/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.4813\n",
+            "Epoch 173/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.4576\n",
+            "Epoch 174/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.4338\n",
+            "Epoch 175/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 34.4098\n",
+            "Epoch 176/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.3857\n",
+            "Epoch 177/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 34.3615\n",
+            "Epoch 178/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.3371\n",
+            "Epoch 179/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 34.3125\n",
+            "Epoch 180/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 34.2878\n",
+            "Epoch 181/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 34.2629\n",
+            "Epoch 182/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 34.2379\n",
+            "Epoch 183/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 34.2127\n",
+            "Epoch 184/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 34.1873\n",
+            "Epoch 185/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 34.1618\n",
+            "Epoch 186/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.1360\n",
+            "Epoch 187/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 34.1102\n",
+            "Epoch 188/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.0841\n",
+            "Epoch 189/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 34.0580\n",
+            "Epoch 190/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 34.0317\n",
+            "Epoch 191/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 34.0053\n",
+            "Epoch 192/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 33.9789\n",
+            "Epoch 193/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.9524\n",
+            "Epoch 194/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.9259\n",
+            "Epoch 195/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.8994\n",
+            "Epoch 196/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.8730\n",
+            "Epoch 197/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.8467\n",
+            "Epoch 198/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.8206\n",
+            "Epoch 199/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.7947\n",
+            "Epoch 200/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.7692\n",
+            "Epoch 201/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 33.7440\n",
+            "Epoch 202/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.7192\n",
+            "Epoch 203/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.6948\n",
+            "Epoch 204/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.6711\n",
+            "Epoch 205/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.6479\n",
+            "Epoch 206/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.6253\n",
+            "Epoch 207/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 33.6034\n",
+            "Epoch 208/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.5823\n",
+            "Epoch 209/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.5618\n",
+            "Epoch 210/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.5421\n",
+            "Epoch 211/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.5232\n",
+            "Epoch 212/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.5049\n",
+            "Epoch 213/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.4874\n",
+            "Epoch 214/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.4705\n",
+            "Epoch 215/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.4543\n",
+            "Epoch 216/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.4387\n",
+            "Epoch 217/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.4236\n",
+            "Epoch 218/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 33.4090\n",
+            "Epoch 219/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.3949\n",
+            "Epoch 220/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.3811\n",
+            "Epoch 221/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.3677\n",
+            "Epoch 222/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.3546\n",
+            "Epoch 223/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.3417\n",
+            "Epoch 224/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.3290\n",
+            "Epoch 225/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 33.3165\n",
+            "Epoch 226/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 33.3041\n",
+            "Epoch 227/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 33.2919\n",
+            "Epoch 228/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 33.2797\n",
+            "Epoch 229/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 33.2676\n",
+            "Epoch 230/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 33.2555\n",
+            "Epoch 231/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.2435\n",
+            "Epoch 232/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 33.2314\n",
+            "Epoch 233/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.2194\n",
+            "Epoch 234/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 33.2073\n",
+            "Epoch 235/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.1953\n",
+            "Epoch 236/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 33.1832\n",
+            "Epoch 237/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 33.1712\n",
+            "Epoch 238/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.1590\n",
+            "Epoch 239/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.1469\n",
+            "Epoch 240/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.1348\n",
+            "Epoch 241/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.1226\n",
+            "Epoch 242/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 33.1104\n",
+            "Epoch 243/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 33.0982\n",
+            "Epoch 244/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.0860\n",
+            "Epoch 245/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.0737\n",
+            "Epoch 246/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 33.0615\n",
+            "Epoch 247/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.0492\n",
+            "Epoch 248/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 33.0369\n",
+            "Epoch 249/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.0246\n",
+            "Epoch 250/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 33.0123\n",
+            "Epoch 251/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 33.0000\n",
+            "Epoch 252/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.9876\n",
+            "Epoch 253/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.9753\n",
+            "Epoch 254/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 32.9629\n",
+            "Epoch 255/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 32.9506\n",
+            "Epoch 256/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.9382\n",
+            "Epoch 257/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 32.9258\n",
+            "Epoch 258/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 32.9135\n",
+            "Epoch 259/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.9011\n",
+            "Epoch 260/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.8887\n",
+            "Epoch 261/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 32.8763\n",
+            "Epoch 262/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 32.8639\n",
+            "Epoch 263/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.8515\n",
+            "Epoch 264/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 32.8391\n",
+            "Epoch 265/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.8267\n",
+            "Epoch 266/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 32.8143\n",
+            "Epoch 267/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 32.8019\n",
+            "Epoch 268/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.7894\n",
+            "Epoch 269/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.7770\n",
+            "Epoch 270/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 32.7645\n",
+            "Epoch 271/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.7521\n",
+            "Epoch 272/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.7396\n",
+            "Epoch 273/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.7271\n",
+            "Epoch 274/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.7146\n",
+            "Epoch 275/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.7021\n",
+            "Epoch 276/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.6896\n",
+            "Epoch 277/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.6771\n",
+            "Epoch 278/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.6645\n",
+            "Epoch 279/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.6520\n",
+            "Epoch 280/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.6394\n",
+            "Epoch 281/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 32.6268\n",
+            "Epoch 282/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.6142\n",
+            "Epoch 283/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.6016\n",
+            "Epoch 284/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 32.5890\n",
+            "Epoch 285/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 32.5764\n",
+            "Epoch 286/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.5637\n",
+            "Epoch 287/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.5510\n",
+            "Epoch 288/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.5383\n",
+            "Epoch 289/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.5256\n",
+            "Epoch 290/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.5129\n",
+            "Epoch 291/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 32.5002\n",
+            "Epoch 292/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 32.4874\n",
+            "Epoch 293/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.4747\n",
+            "Epoch 294/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 32.4619\n",
+            "Epoch 295/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.4491\n",
+            "Epoch 296/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.4363\n",
+            "Epoch 297/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 32.4234\n",
+            "Epoch 298/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.4106\n",
+            "Epoch 299/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 32.3977\n",
+            "Epoch 300/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 32.3848\n",
+            "Epoch 301/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 32.3719\n",
+            "Epoch 302/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.3590\n",
+            "Epoch 303/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.3460\n",
+            "Epoch 304/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.3331\n",
+            "Epoch 305/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.3201\n",
+            "Epoch 306/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.3071\n",
+            "Epoch 307/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.2940\n",
+            "Epoch 308/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 32.2810\n",
+            "Epoch 309/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.2679\n",
+            "Epoch 310/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 32.2548\n",
+            "Epoch 311/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.2417\n",
+            "Epoch 312/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.2286\n",
+            "Epoch 313/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.2154\n",
+            "Epoch 314/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 32.2022\n",
+            "Epoch 315/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.1890\n",
+            "Epoch 316/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.1758\n",
+            "Epoch 317/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.1626\n",
+            "Epoch 318/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 32.1493\n",
+            "Epoch 319/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 32.1360\n",
+            "Epoch 320/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.1227\n",
+            "Epoch 321/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.1093\n",
+            "Epoch 322/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.0960\n",
+            "Epoch 323/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 32.0826\n",
+            "Epoch 324/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 32.0691\n",
+            "Epoch 325/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 32.0557\n",
+            "Epoch 326/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.0422\n",
+            "Epoch 327/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 32.0287\n",
+            "Epoch 328/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.0152\n",
+            "Epoch 329/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 32.0017\n",
+            "Epoch 330/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 31.9881\n",
+            "Epoch 331/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 31.9745\n",
+            "Epoch 332/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 31.9608\n",
+            "Epoch 333/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 31.9472\n",
+            "Epoch 334/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 31.9335\n",
+            "Epoch 335/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 31.9198\n",
+            "Epoch 336/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 31.9060\n",
+            "Epoch 337/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 31.8922\n",
+            "Epoch 338/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 31.8784\n",
+            "Epoch 339/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 31.8646\n",
+            "Epoch 340/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 31.8507\n",
+            "Epoch 341/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 31.8368\n",
+            "Epoch 342/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.8229\n",
+            "Epoch 343/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 31.8089\n",
+            "Epoch 344/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 31.7949\n",
+            "Epoch 345/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 31.7809\n",
+            "Epoch 346/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 31.7668\n",
+            "Epoch 347/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 31.7527\n",
+            "Epoch 348/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 31.7386\n",
+            "Epoch 349/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 31.7244\n",
+            "Epoch 350/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.7102\n",
+            "Epoch 351/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 31.6960\n",
+            "Epoch 352/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 31.6817\n",
+            "Epoch 353/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 31.6674\n",
+            "Epoch 354/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 31.6531\n",
+            "Epoch 355/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 31.6387\n",
+            "Epoch 356/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 31.6243\n",
+            "Epoch 357/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 31.6099\n",
+            "Epoch 358/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 31.5954\n",
+            "Epoch 359/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 31.5809\n",
+            "Epoch 360/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 31.5663\n",
+            "Epoch 361/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 31.5517\n",
+            "Epoch 362/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 31.5371\n",
+            "Epoch 363/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 31.5225\n",
+            "Epoch 364/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 31.5077\n",
+            "Epoch 365/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 31.4930\n",
+            "Epoch 366/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 31.4782\n",
+            "Epoch 367/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 31.4634\n",
+            "Epoch 368/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 31.4485\n",
+            "Epoch 369/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 31.4336\n",
+            "Epoch 370/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 31.4187\n",
+            "Epoch 371/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 31.4037\n",
+            "Epoch 372/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 31.3887\n",
+            "Epoch 373/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 31.3736\n",
+            "Epoch 374/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 31.3585\n",
+            "Epoch 375/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.3434\n",
+            "Epoch 376/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 31.3282\n",
+            "Epoch 377/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 31.3130\n",
+            "Epoch 378/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 31.2977\n",
+            "Epoch 379/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 31.2824\n",
+            "Epoch 380/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 31.2671\n",
+            "Epoch 381/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 31.2517\n",
+            "Epoch 382/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 31.2362\n",
+            "Epoch 383/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.2208\n",
+            "Epoch 384/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 31.2052\n",
+            "Epoch 385/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 31.1897\n",
+            "Epoch 386/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 31.1741\n",
+            "Epoch 387/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 31.1584\n",
+            "Epoch 388/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 31.1427\n",
+            "Epoch 389/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 31.1270\n",
+            "Epoch 390/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 31.1112\n",
+            "Epoch 391/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 31.0954\n",
+            "Epoch 392/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 31.0795\n",
+            "Epoch 393/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 31.0636\n",
+            "Epoch 394/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 31.0476\n",
+            "Epoch 395/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.0316\n",
+            "Epoch 396/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 31.0156\n",
+            "Epoch 397/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.9995\n",
+            "Epoch 398/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 30.9834\n",
+            "Epoch 399/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 30.9672\n",
+            "Epoch 400/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 30.9510\n",
+            "Epoch 401/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 30.9347\n",
+            "Epoch 402/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.9184\n",
+            "Epoch 403/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 30.9021\n",
+            "Epoch 404/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 30.8857\n",
+            "Epoch 405/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.8693\n",
+            "Epoch 406/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 30.8528\n",
+            "Epoch 407/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.8363\n",
+            "Epoch 408/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 30.8197\n",
+            "Epoch 409/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.8031\n",
+            "Epoch 410/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.7864\n",
+            "Epoch 411/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.7698\n",
+            "Epoch 412/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.7530\n",
+            "Epoch 413/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 30.7362\n",
+            "Epoch 414/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 30.7194\n",
+            "Epoch 415/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.7026\n",
+            "Epoch 416/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 30.6857\n",
+            "Epoch 417/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.6687\n",
+            "Epoch 418/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.6517\n",
+            "Epoch 419/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 30.6347\n",
+            "Epoch 420/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 30.6177\n",
+            "Epoch 421/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.6005\n",
+            "Epoch 422/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 30.5834\n",
+            "Epoch 423/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 30.5662\n",
+            "Epoch 424/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 30.5490\n",
+            "Epoch 425/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.5317\n",
+            "Epoch 426/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.5144\n",
+            "Epoch 427/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.4971\n",
+            "Epoch 428/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.4797\n",
+            "Epoch 429/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.4623\n",
+            "Epoch 430/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.4448\n",
+            "Epoch 431/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 30.4273\n",
+            "Epoch 432/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.4098\n",
+            "Epoch 433/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 30.3923\n",
+            "Epoch 434/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.3747\n",
+            "Epoch 435/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.3570\n",
+            "Epoch 436/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.3394\n",
+            "Epoch 437/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.3216\n",
+            "Epoch 438/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.3039\n",
+            "Epoch 439/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.2861\n",
+            "Epoch 440/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.2683\n",
+            "Epoch 441/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.2505\n",
+            "Epoch 442/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 30.2326\n",
+            "Epoch 443/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.2147\n",
+            "Epoch 444/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 30.1967\n",
+            "Epoch 445/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 30.1788\n",
+            "Epoch 446/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 30.1607\n",
+            "Epoch 447/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 30.1427\n",
+            "Epoch 448/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.1246\n",
+            "Epoch 449/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 30.1065\n",
+            "Epoch 450/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 30.0884\n",
+            "Epoch 451/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.0702\n",
+            "Epoch 452/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.0520\n",
+            "Epoch 453/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 30.0338\n",
+            "Epoch 454/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 30.0156\n",
+            "Epoch 455/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.9973\n",
+            "Epoch 456/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.9790\n",
+            "Epoch 457/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.9606\n",
+            "Epoch 458/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.9422\n",
+            "Epoch 459/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.9238\n",
+            "Epoch 460/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.9054\n",
+            "Epoch 461/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 29.8870\n",
+            "Epoch 462/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.8685\n",
+            "Epoch 463/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.8500\n",
+            "Epoch 464/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.8314\n",
+            "Epoch 465/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.8129\n",
+            "Epoch 466/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.7943\n",
+            "Epoch 467/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 29.7757\n",
+            "Epoch 468/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 29.7570\n",
+            "Epoch 469/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 29.7383\n",
+            "Epoch 470/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.7197\n",
+            "Epoch 471/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.7009\n",
+            "Epoch 472/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.6822\n",
+            "Epoch 473/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 29.6634\n",
+            "Epoch 474/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.6446\n",
+            "Epoch 475/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.6258\n",
+            "Epoch 476/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 29.6069\n",
+            "Epoch 477/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.5881\n",
+            "Epoch 478/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.5692\n",
+            "Epoch 479/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.5503\n",
+            "Epoch 480/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.5313\n",
+            "Epoch 481/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 29.5123\n",
+            "Epoch 482/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.4934\n",
+            "Epoch 483/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.4743\n",
+            "Epoch 484/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.4553\n",
+            "Epoch 485/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 29.4362\n",
+            "Epoch 486/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.4171\n",
+            "Epoch 487/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 29.3980\n",
+            "Epoch 488/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.3789\n",
+            "Epoch 489/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 29.3598\n",
+            "Epoch 490/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 29.3406\n",
+            "Epoch 491/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 29.3214\n",
+            "Epoch 492/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 29.3022\n",
+            "Epoch 493/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.2829\n",
+            "Epoch 494/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.2636\n",
+            "Epoch 495/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.2443\n",
+            "Epoch 496/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 29.2250\n",
+            "Epoch 497/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.2057\n",
+            "Epoch 498/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.1863\n",
+            "Epoch 499/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.1670\n",
+            "Epoch 500/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.1476\n",
+            "Epoch 501/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 29.1281\n",
+            "Epoch 502/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 29.1087\n",
+            "Epoch 503/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 29.0892\n",
+            "Epoch 504/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 29.0698\n",
+            "Epoch 505/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.0503\n",
+            "Epoch 506/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 29.0307\n",
+            "Epoch 507/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 29.0112\n",
+            "Epoch 508/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.9916\n",
+            "Epoch 509/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 28.9720\n",
+            "Epoch 510/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 28.9524\n",
+            "Epoch 511/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.9328\n",
+            "Epoch 512/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.9131\n",
+            "Epoch 513/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.8935\n",
+            "Epoch 514/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.8738\n",
+            "Epoch 515/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 28.8541\n",
+            "Epoch 516/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.8343\n",
+            "Epoch 517/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.8146\n",
+            "Epoch 518/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.7948\n",
+            "Epoch 519/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.7750\n",
+            "Epoch 520/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.7552\n",
+            "Epoch 521/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.7354\n",
+            "Epoch 522/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.7156\n",
+            "Epoch 523/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.6957\n",
+            "Epoch 524/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.6758\n",
+            "Epoch 525/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.6559\n",
+            "Epoch 526/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.6360\n",
+            "Epoch 527/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.6161\n",
+            "Epoch 528/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 28.5961\n",
+            "Epoch 529/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 28.5761\n",
+            "Epoch 530/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 28.5561\n",
+            "Epoch 531/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.5361\n",
+            "Epoch 532/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 28.5161\n",
+            "Epoch 533/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.4961\n",
+            "Epoch 534/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.4760\n",
+            "Epoch 535/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 28.4559\n",
+            "Epoch 536/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.4358\n",
+            "Epoch 537/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.4157\n",
+            "Epoch 538/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 28.3956\n",
+            "Epoch 539/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 28.3754\n",
+            "Epoch 540/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 28.3553\n",
+            "Epoch 541/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.3351\n",
+            "Epoch 542/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 28.3149\n",
+            "Epoch 543/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.2947\n",
+            "Epoch 544/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.2745\n",
+            "Epoch 545/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.2543\n",
+            "Epoch 546/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 28.2340\n",
+            "Epoch 547/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 28.2137\n",
+            "Epoch 548/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 28.1935\n",
+            "Epoch 549/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.1732\n",
+            "Epoch 550/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.1528\n",
+            "Epoch 551/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.1325\n",
+            "Epoch 552/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.1122\n",
+            "Epoch 553/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.0918\n",
+            "Epoch 554/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 28.0715\n",
+            "Epoch 555/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.0511\n",
+            "Epoch 556/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 28.0307\n",
+            "Epoch 557/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 28.0103\n",
+            "Epoch 558/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.9899\n",
+            "Epoch 559/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 27.9694\n",
+            "Epoch 560/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 27.9490\n",
+            "Epoch 561/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.9286\n",
+            "Epoch 562/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 27.9081\n",
+            "Epoch 563/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.8876\n",
+            "Epoch 564/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 27.8671\n",
+            "Epoch 565/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 27.8466\n",
+            "Epoch 566/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.8261\n",
+            "Epoch 567/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.8056\n",
+            "Epoch 568/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.7850\n",
+            "Epoch 569/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 27.7645\n",
+            "Epoch 570/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.7440\n",
+            "Epoch 571/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 27.7234\n",
+            "Epoch 572/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 27.7028\n",
+            "Epoch 573/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.6822\n",
+            "Epoch 574/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.6616\n",
+            "Epoch 575/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 27.6410\n",
+            "Epoch 576/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 27.6204\n",
+            "Epoch 577/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 27.5998\n",
+            "Epoch 578/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.5792\n",
+            "Epoch 579/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.5585\n",
+            "Epoch 580/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.5379\n",
+            "Epoch 581/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.5173\n",
+            "Epoch 582/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 27.4966\n",
+            "Epoch 583/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 27.4759\n",
+            "Epoch 584/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 27.4552\n",
+            "Epoch 585/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.4346\n",
+            "Epoch 586/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 27.4139\n",
+            "Epoch 587/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 27.3932\n",
+            "Epoch 588/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.3725\n",
+            "Epoch 589/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.3518\n",
+            "Epoch 590/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.3311\n",
+            "Epoch 591/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.3103\n",
+            "Epoch 592/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.2896\n",
+            "Epoch 593/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.2689\n",
+            "Epoch 594/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 27.2481\n",
+            "Epoch 595/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 27.2274\n",
+            "Epoch 596/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.2067\n",
+            "Epoch 597/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.1859\n",
+            "Epoch 598/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 27.1652\n",
+            "Epoch 599/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 27.1444\n",
+            "Epoch 600/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 27.1236\n",
+            "Epoch 601/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 27.1029\n",
+            "Epoch 602/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 27.0821\n",
+            "Epoch 603/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 27.0613\n",
+            "Epoch 604/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 27.0406\n",
+            "Epoch 605/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 27.0198\n",
+            "Epoch 606/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 26.9990\n",
+            "Epoch 607/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 26.9782\n",
+            "Epoch 608/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 26.9574\n",
+            "Epoch 609/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 26.9366\n",
+            "Epoch 610/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 26.9159\n",
+            "Epoch 611/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 26.8951\n",
+            "Epoch 612/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 26.8743\n",
+            "Epoch 613/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 26.8535\n",
+            "Epoch 614/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 26.8327\n",
+            "Epoch 615/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 26.8119\n",
+            "Epoch 616/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 26.7911\n",
+            "Epoch 617/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 26.7703\n",
+            "Epoch 618/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 26.7495\n",
+            "Epoch 619/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 26.7287\n",
+            "Epoch 620/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 26.7079\n",
+            "Epoch 621/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 26.6871\n",
+            "Epoch 622/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 26.6663\n",
+            "Epoch 623/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 26.6455\n",
+            "Epoch 624/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 26.6247\n",
+            "Epoch 625/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 26.6039\n",
+            "Epoch 626/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 26.5831\n",
+            "Epoch 627/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 26.5623\n",
+            "Epoch 628/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 26.5415\n",
+            "Epoch 629/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 26.5208\n",
+            "Epoch 630/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 26.5000\n",
+            "Epoch 631/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 26.4792\n",
+            "Epoch 632/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 26.4584\n",
+            "Epoch 633/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 26.4376\n",
+            "Epoch 634/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 26.4168\n",
+            "Epoch 635/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 26.3961\n",
+            "Epoch 636/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 26.3753\n",
+            "Epoch 637/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 26.3545\n",
+            "Epoch 638/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 26.3337\n",
+            "Epoch 639/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 26.3130\n",
+            "Epoch 640/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 26.2922\n",
+            "Epoch 641/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 26.2715\n",
+            "Epoch 642/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 26.2507\n",
+            "Epoch 643/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 26.2300\n",
+            "Epoch 644/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 26.2092\n",
+            "Epoch 645/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 26.1885\n",
+            "Epoch 646/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 26.1677\n",
+            "Epoch 647/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 26.1470\n",
+            "Epoch 648/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 26.1263\n",
+            "Epoch 649/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 26.1055\n",
+            "Epoch 650/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 26.0848\n",
+            "Epoch 651/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 26.0641\n",
+            "Epoch 652/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 26.0434\n",
+            "Epoch 653/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 26.0227\n",
+            "Epoch 654/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 26.0020\n",
+            "Epoch 655/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.9813\n",
+            "Epoch 656/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.9606\n",
+            "Epoch 657/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.9399\n",
+            "Epoch 658/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.9193\n",
+            "Epoch 659/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 25.8986\n",
+            "Epoch 660/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.8779\n",
+            "Epoch 661/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.8573\n",
+            "Epoch 662/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.8366\n",
+            "Epoch 663/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.8160\n",
+            "Epoch 664/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 25.7954\n",
+            "Epoch 665/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 25.7747\n",
+            "Epoch 666/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 25.7541\n",
+            "Epoch 667/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 25.7335\n",
+            "Epoch 668/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.7129\n",
+            "Epoch 669/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.6923\n",
+            "Epoch 670/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 25.6717\n",
+            "Epoch 671/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.6511\n",
+            "Epoch 672/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.6305\n",
+            "Epoch 673/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.6100\n",
+            "Epoch 674/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.5894\n",
+            "Epoch 675/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.5689\n",
+            "Epoch 676/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 25.5483\n",
+            "Epoch 677/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 25.5278\n",
+            "Epoch 678/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.5072\n",
+            "Epoch 679/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.4867\n",
+            "Epoch 680/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.4662\n",
+            "Epoch 681/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 25.4457\n",
+            "Epoch 682/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.4252\n",
+            "Epoch 683/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.4047\n",
+            "Epoch 684/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.3843\n",
+            "Epoch 685/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 25.3638\n",
+            "Epoch 686/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.3433\n",
+            "Epoch 687/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 25.3229\n",
+            "Epoch 688/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.3024\n",
+            "Epoch 689/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.2820\n",
+            "Epoch 690/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 25.2616\n",
+            "Epoch 691/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.2412\n",
+            "Epoch 692/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 25.2208\n",
+            "Epoch 693/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.2004\n",
+            "Epoch 694/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 25.1800\n",
+            "Epoch 695/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 25.1596\n",
+            "Epoch 696/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 25.1393\n",
+            "Epoch 697/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 25.1189\n",
+            "Epoch 698/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.0986\n",
+            "Epoch 699/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 25.0782\n",
+            "Epoch 700/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 25.0579\n",
+            "Epoch 701/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 25.0376\n",
+            "Epoch 702/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 25.0173\n",
+            "Epoch 703/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.9970\n",
+            "Epoch 704/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 24.9767\n",
+            "Epoch 705/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 24.9565\n",
+            "Epoch 706/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 24.9362\n",
+            "Epoch 707/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.9160\n",
+            "Epoch 708/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.8957\n",
+            "Epoch 709/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 24.8755\n",
+            "Epoch 710/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.8553\n",
+            "Epoch 711/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.8351\n",
+            "Epoch 712/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 24.8149\n",
+            "Epoch 713/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.7947\n",
+            "Epoch 714/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.7746\n",
+            "Epoch 715/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 24.7544\n",
+            "Epoch 716/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 24.7343\n",
+            "Epoch 717/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.7141\n",
+            "Epoch 718/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.6940\n",
+            "Epoch 719/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.6739\n",
+            "Epoch 720/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.6538\n",
+            "Epoch 721/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.6337\n",
+            "Epoch 722/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.6136\n",
+            "Epoch 723/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.5936\n",
+            "Epoch 724/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.5735\n",
+            "Epoch 725/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.5535\n",
+            "Epoch 726/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.5335\n",
+            "Epoch 727/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.5134\n",
+            "Epoch 728/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.4934\n",
+            "Epoch 729/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.4734\n",
+            "Epoch 730/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.4535\n",
+            "Epoch 731/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 24.4335\n",
+            "Epoch 732/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.4135\n",
+            "Epoch 733/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.3936\n",
+            "Epoch 734/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 24.3737\n",
+            "Epoch 735/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.3537\n",
+            "Epoch 736/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.3338\n",
+            "Epoch 737/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.3139\n",
+            "Epoch 738/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 24.2941\n",
+            "Epoch 739/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 24.2742\n",
+            "Epoch 740/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.2543\n",
+            "Epoch 741/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.2345\n",
+            "Epoch 742/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.2147\n",
+            "Epoch 743/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 24.1949\n",
+            "Epoch 744/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.1751\n",
+            "Epoch 745/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.1553\n",
+            "Epoch 746/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.1355\n",
+            "Epoch 747/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 24.1157\n",
+            "Epoch 748/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 24.0960\n",
+            "Epoch 749/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 24.0762\n",
+            "Epoch 750/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.0565\n",
+            "Epoch 751/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 24.0368\n",
+            "Epoch 752/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 24.0171\n",
+            "Epoch 753/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 23.9974\n",
+            "Epoch 754/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.9777\n",
+            "Epoch 755/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.9581\n",
+            "Epoch 756/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.9384\n",
+            "Epoch 757/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.9188\n",
+            "Epoch 758/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.8992\n",
+            "Epoch 759/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 23.8796\n",
+            "Epoch 760/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.8600\n",
+            "Epoch 761/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.8404\n",
+            "Epoch 762/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 23.8208\n",
+            "Epoch 763/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.8013\n",
+            "Epoch 764/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 23.7817\n",
+            "Epoch 765/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.7622\n",
+            "Epoch 766/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 23.7427\n",
+            "Epoch 767/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 23.7232\n",
+            "Epoch 768/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.7037\n",
+            "Epoch 769/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.6842\n",
+            "Epoch 770/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.6648\n",
+            "Epoch 771/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 23.6453\n",
+            "Epoch 772/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.6259\n",
+            "Epoch 773/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.6065\n",
+            "Epoch 774/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 23.5871\n",
+            "Epoch 775/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 23.5677\n",
+            "Epoch 776/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 23.5483\n",
+            "Epoch 777/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.5289\n",
+            "Epoch 778/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.5096\n",
+            "Epoch 779/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 23.4902\n",
+            "Epoch 780/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 23.4709\n",
+            "Epoch 781/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.4516\n",
+            "Epoch 782/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.4323\n",
+            "Epoch 783/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.4130\n",
+            "Epoch 784/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 23.3938\n",
+            "Epoch 785/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.3745\n",
+            "Epoch 786/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 23.3553\n",
+            "Epoch 787/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.3360\n",
+            "Epoch 788/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.3168\n",
+            "Epoch 789/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 23.2976\n",
+            "Epoch 790/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.2785\n",
+            "Epoch 791/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.2593\n",
+            "Epoch 792/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.2401\n",
+            "Epoch 793/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 23.2210\n",
+            "Epoch 794/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 23.2019\n",
+            "Epoch 795/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.1827\n",
+            "Epoch 796/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.1636\n",
+            "Epoch 797/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.1446\n",
+            "Epoch 798/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 23.1255\n",
+            "Epoch 799/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 23.1064\n",
+            "Epoch 800/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 23.0874\n",
+            "Epoch 801/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 23.0683\n",
+            "Epoch 802/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 23.0493\n",
+            "Epoch 803/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 23.0303\n",
+            "Epoch 804/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 23.0113\n",
+            "Epoch 805/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 22.9924\n",
+            "Epoch 806/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.9734\n",
+            "Epoch 807/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.9545\n",
+            "Epoch 808/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 22.9355\n",
+            "Epoch 809/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 22.9166\n",
+            "Epoch 810/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 22.8977\n",
+            "Epoch 811/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 22.8788\n",
+            "Epoch 812/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 22.8599\n",
+            "Epoch 813/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 22.8411\n",
+            "Epoch 814/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 22.8222\n",
+            "Epoch 815/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.8034\n",
+            "Epoch 816/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 22.7846\n",
+            "Epoch 817/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 22.7658\n",
+            "Epoch 818/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.7470\n",
+            "Epoch 819/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 22.7282\n",
+            "Epoch 820/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 22.7094\n",
+            "Epoch 821/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.6907\n",
+            "Epoch 822/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.6719\n",
+            "Epoch 823/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.6532\n",
+            "Epoch 824/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 22.6345\n",
+            "Epoch 825/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 22.6158\n",
+            "Epoch 826/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 22.5971\n",
+            "Epoch 827/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.5785\n",
+            "Epoch 828/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.5598\n",
+            "Epoch 829/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 22.5412\n",
+            "Epoch 830/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 22.5226\n",
+            "Epoch 831/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 22.5040\n",
+            "Epoch 832/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 22.4854\n",
+            "Epoch 833/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 22.4668\n",
+            "Epoch 834/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.4482\n",
+            "Epoch 835/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 22.4297\n",
+            "Epoch 836/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.4111\n",
+            "Epoch 837/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 22.3926\n",
+            "Epoch 838/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 22.3741\n",
+            "Epoch 839/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 22.3556\n",
+            "Epoch 840/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 22.3371\n",
+            "Epoch 841/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 22.3187\n",
+            "Epoch 842/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 22.3002\n",
+            "Epoch 843/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 22.2818\n",
+            "Epoch 844/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 22.2633\n",
+            "Epoch 845/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 22.2449\n",
+            "Epoch 846/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 22.2265\n",
+            "Epoch 847/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 22.2082\n",
+            "Epoch 848/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 22.1898\n",
+            "Epoch 849/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 22.1714\n",
+            "Epoch 850/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 22.1531\n",
+            "Epoch 851/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 22.1348\n",
+            "Epoch 852/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 22.1164\n",
+            "Epoch 853/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 22.0982\n",
+            "Epoch 854/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 22.0799\n",
+            "Epoch 855/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 22.0616\n",
+            "Epoch 856/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 22.0433\n",
+            "Epoch 857/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 22.0251\n",
+            "Epoch 858/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 22.0069\n",
+            "Epoch 859/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 21.9887\n",
+            "Epoch 860/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 21.9705\n",
+            "Epoch 861/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 21.9523\n",
+            "Epoch 862/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 21.9341\n",
+            "Epoch 863/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 21.9159\n",
+            "Epoch 864/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 21.8978\n",
+            "Epoch 865/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 21.8797\n",
+            "Epoch 866/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 21.8616\n",
+            "Epoch 867/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 21.8435\n",
+            "Epoch 868/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 21.8254\n",
+            "Epoch 869/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 21.8073\n",
+            "Epoch 870/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 21.7892\n",
+            "Epoch 871/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 21.7712\n",
+            "Epoch 872/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 21.7532\n",
+            "Epoch 873/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 21.7351\n",
+            "Epoch 874/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 21.7171\n",
+            "Epoch 875/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 21.6991\n",
+            "Epoch 876/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 21.6812\n",
+            "Epoch 877/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 21.6632\n",
+            "Epoch 878/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 21.6453\n",
+            "Epoch 879/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 21.6273\n",
+            "Epoch 880/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 21.6094\n",
+            "Epoch 881/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 21.5915\n",
+            "Epoch 882/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.5736\n",
+            "Epoch 883/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 21.5557\n",
+            "Epoch 884/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 21.5379\n",
+            "Epoch 885/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 21.5200\n",
+            "Epoch 886/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 21.5022\n",
+            "Epoch 887/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 21.4843\n",
+            "Epoch 888/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.4665\n",
+            "Epoch 889/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 21.4487\n",
+            "Epoch 890/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 21.4310\n",
+            "Epoch 891/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 21.4132\n",
+            "Epoch 892/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 21.3954\n",
+            "Epoch 893/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 21.3777\n",
+            "Epoch 894/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 21.3600\n",
+            "Epoch 895/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 21.3422\n",
+            "Epoch 896/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 21.3245\n",
+            "Epoch 897/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 21.3069\n",
+            "Epoch 898/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.2892\n",
+            "Epoch 899/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.2715\n",
+            "Epoch 900/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 21.2539\n",
+            "Epoch 901/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 21.2363\n",
+            "Epoch 902/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 21.2186\n",
+            "Epoch 903/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.2010\n",
+            "Epoch 904/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 21.1834\n",
+            "Epoch 905/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 21.1659\n",
+            "Epoch 906/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.1483\n",
+            "Epoch 907/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 21.1308\n",
+            "Epoch 908/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 21.1132\n",
+            "Epoch 909/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 21.0957\n",
+            "Epoch 910/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 21.0782\n",
+            "Epoch 911/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 21.0607\n",
+            "Epoch 912/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 21.0432\n",
+            "Epoch 913/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 21.0257\n",
+            "Epoch 914/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 21.0083\n",
+            "Epoch 915/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 20.9909\n",
+            "Epoch 916/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.9734\n",
+            "Epoch 917/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.9560\n",
+            "Epoch 918/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 20.9386\n",
+            "Epoch 919/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.9212\n",
+            "Epoch 920/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.9039\n",
+            "Epoch 921/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.8865\n",
+            "Epoch 922/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.8692\n",
+            "Epoch 923/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 20.8518\n",
+            "Epoch 924/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 20.8345\n",
+            "Epoch 925/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 20.8172\n",
+            "Epoch 926/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.7999\n",
+            "Epoch 927/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 20.7826\n",
+            "Epoch 928/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 20.7654\n",
+            "Epoch 929/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 20.7481\n",
+            "Epoch 930/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 20.7309\n",
+            "Epoch 931/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 20.7136\n",
+            "Epoch 932/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.6964\n",
+            "Epoch 933/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.6792\n",
+            "Epoch 934/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 20.6621\n",
+            "Epoch 935/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.6449\n",
+            "Epoch 936/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 20.6277\n",
+            "Epoch 937/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.6106\n",
+            "Epoch 938/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.5934\n",
+            "Epoch 939/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.5763\n",
+            "Epoch 940/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 20.5592\n",
+            "Epoch 941/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 20.5421\n",
+            "Epoch 942/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.5251\n",
+            "Epoch 943/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 20.5080\n",
+            "Epoch 944/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.4909\n",
+            "Epoch 945/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.4739\n",
+            "Epoch 946/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 20.4569\n",
+            "Epoch 947/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 20.4399\n",
+            "Epoch 948/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 20.4229\n",
+            "Epoch 949/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 20.4059\n",
+            "Epoch 950/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.3889\n",
+            "Epoch 951/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.3719\n",
+            "Epoch 952/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.3550\n",
+            "Epoch 953/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.3381\n",
+            "Epoch 954/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.3211\n",
+            "Epoch 955/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.3042\n",
+            "Epoch 956/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.2873\n",
+            "Epoch 957/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.2705\n",
+            "Epoch 958/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.2536\n",
+            "Epoch 959/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.2367\n",
+            "Epoch 960/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.2199\n",
+            "Epoch 961/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 20.2031\n",
+            "Epoch 962/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 20.1863\n",
+            "Epoch 963/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.1695\n",
+            "Epoch 964/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.1527\n",
+            "Epoch 965/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 20.1359\n",
+            "Epoch 966/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 20.1191\n",
+            "Epoch 967/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 20.1024\n",
+            "Epoch 968/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 20.0856\n",
+            "Epoch 969/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.0689\n",
+            "Epoch 970/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.0522\n",
+            "Epoch 971/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 20.0355\n",
+            "Epoch 972/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 20.0188\n",
+            "Epoch 973/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 20.0021\n",
+            "Epoch 974/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 19.9855\n",
+            "Epoch 975/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 19.9688\n",
+            "Epoch 976/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 19.9522\n",
+            "Epoch 977/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.9356\n",
+            "Epoch 978/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 19.9190\n",
+            "Epoch 979/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 19.9024\n",
+            "Epoch 980/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 19.8858\n",
+            "Epoch 981/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.8692\n",
+            "Epoch 982/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.8527\n",
+            "Epoch 983/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 19.8361\n",
+            "Epoch 984/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 19.8196\n",
+            "Epoch 985/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.8031\n",
+            "Epoch 986/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 19.7866\n",
+            "Epoch 987/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.7701\n",
+            "Epoch 988/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 19.7536\n",
+            "Epoch 989/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 19.7371\n",
+            "Epoch 990/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 19.7207\n",
+            "Epoch 991/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 19.7042\n",
+            "Epoch 992/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 19.6878\n",
+            "Epoch 993/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.6714\n",
+            "Epoch 994/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.6550\n",
+            "Epoch 995/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.6386\n",
+            "Epoch 996/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 19.6222\n",
+            "Epoch 997/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 19.6058\n",
+            "Epoch 998/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 19.5895\n",
+            "Epoch 999/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.5731\n",
+            "Epoch 1000/1000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 19.5568\n",
+            "Epoch 1000/1000\n",
+            " - loss: 19.5568\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 19.5568\n",
+            "Restoring model weights from the end of the best epoch: 1000.\n",
+            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# обучение AE1\n",
+        "patience = 300\n",
+        "ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt',\n",
+        "1000, True, patience)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ae9e6816-d300-455a-a4ee-77e4911e02f5",
+      "metadata": {
+        "id": "ae9e6816-d300-455a-a4ee-77e4911e02f5"
+      },
+      "source": [
+        "### 4) Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ошибка MSE_AE1 = 19.5568"
+      ],
+      "metadata": {
+        "id": "Np8zquNSgtO9"
+      },
+      "id": "Np8zquNSgtO9"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fb7eeae8-f478-4295-b6de-b5790b7861f2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 725
+        },
+        "id": "fb7eeae8-f478-4295-b6de-b5790b7861f2",
+        "outputId": "e77d7472-ae81-478b-ac72-a45e1c3165ca"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE1, IREth1, 'AE1')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "92fb420f-907e-4417-ab34-6d12308bed0b",
+      "metadata": {
+        "id": "92fb420f-907e-4417-ab34-6d12308bed0b"
+      },
+      "source": [
+        "### 5) Создали и обучили второй автокодировщик AE2 с усложненной архитектурой, задав большее количество эпох обучения"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bb9f83e2-fecf-4118-8376-a712726d12d5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bb9f83e2-fecf-4118-8376-a712726d12d5",
+        "outputId": "31bcf5ff-071d-4cc2-c40a-560aa19cb2a5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n",
+            "Epoch 88/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 27.6297\n",
+            "Epoch 89/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 27.5561\n",
+            "Epoch 90/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 27.4826\n",
+            "Epoch 91/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 27.4093\n",
+            "Epoch 92/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 27.3362\n",
+            "Epoch 93/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 27.2631\n",
+            "Epoch 94/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 27.1903\n",
+            "Epoch 95/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 27.1176\n",
+            "Epoch 96/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 27.0451\n",
+            "Epoch 97/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 26.9727\n",
+            "Epoch 98/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 26.9005\n",
+            "Epoch 99/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 26.8284\n",
+            "Epoch 100/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 26.7566\n",
+            "Epoch 101/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 26.6849\n",
+            "Epoch 102/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 26.6133\n",
+            "Epoch 103/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 26.5420\n",
+            "Epoch 104/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 26.4708\n",
+            "Epoch 105/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 26.3997\n",
+            "Epoch 106/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 26.3289\n",
+            "Epoch 107/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 26.2582\n",
+            "Epoch 108/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 26.1877\n",
+            "Epoch 109/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 26.1174\n",
+            "Epoch 110/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 26.0473\n",
+            "Epoch 111/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 25.9773\n",
+            "Epoch 112/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 25.9076\n",
+            "Epoch 113/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 25.8380\n",
+            "Epoch 114/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 25.7686\n",
+            "Epoch 115/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 25.6994\n",
+            "Epoch 116/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 25.6303\n",
+            "Epoch 117/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 25.5615\n",
+            "Epoch 118/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 25.4928\n",
+            "Epoch 119/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 25.4244\n",
+            "Epoch 120/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 25.3561\n",
+            "Epoch 121/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 25.2880\n",
+            "Epoch 122/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 25.2201\n",
+            "Epoch 123/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 25.1524\n",
+            "Epoch 124/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 25.0848\n",
+            "Epoch 125/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 25.0175\n",
+            "Epoch 126/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 24.9504\n",
+            "Epoch 127/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 24.8834\n",
+            "Epoch 128/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 24.8167\n",
+            "Epoch 129/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 24.7501\n",
+            "Epoch 130/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 24.6837\n",
+            "Epoch 131/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 24.6175\n",
+            "Epoch 132/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 24.5515\n",
+            "Epoch 133/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 24.4857\n",
+            "Epoch 134/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 24.4201\n",
+            "Epoch 135/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 24.3546\n",
+            "Epoch 136/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 24.2894\n",
+            "Epoch 137/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 24.2243\n",
+            "Epoch 138/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 24.1594\n",
+            "Epoch 139/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 24.0947\n",
+            "Epoch 140/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 24.0302\n",
+            "Epoch 141/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 23.9659\n",
+            "Epoch 142/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 23.9017\n",
+            "Epoch 143/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.8377\n",
+            "Epoch 144/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.7740\n",
+            "Epoch 145/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.7103\n",
+            "Epoch 146/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.6469\n",
+            "Epoch 147/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 23.5836\n",
+            "Epoch 148/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.5205\n",
+            "Epoch 149/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.4576\n",
+            "Epoch 150/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 23.3948\n",
+            "Epoch 151/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 23.3322\n",
+            "Epoch 152/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.2697\n",
+            "Epoch 153/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 23.2074\n",
+            "Epoch 154/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.1453\n",
+            "Epoch 155/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 23.0833\n",
+            "Epoch 156/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 23.0214\n",
+            "Epoch 157/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 22.9597\n",
+            "Epoch 158/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 22.8982\n",
+            "Epoch 159/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 22.8367\n",
+            "Epoch 160/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 22.7754\n",
+            "Epoch 161/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 22.7143\n",
+            "Epoch 162/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 22.6532\n",
+            "Epoch 163/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 22.5923\n",
+            "Epoch 164/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 22.5315\n",
+            "Epoch 165/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 22.4707\n",
+            "Epoch 166/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 22.4101\n",
+            "Epoch 167/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 22.3496\n",
+            "Epoch 168/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.2892\n",
+            "Epoch 169/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 22.2288\n",
+            "Epoch 170/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 22.1686\n",
+            "Epoch 171/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 22.1084\n",
+            "Epoch 172/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 22.0483\n",
+            "Epoch 173/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.9883\n",
+            "Epoch 174/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.9283\n",
+            "Epoch 175/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.8683\n",
+            "Epoch 176/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.8085\n",
+            "Epoch 177/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 21.7486\n",
+            "Epoch 178/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.6888\n",
+            "Epoch 179/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 21.6291\n",
+            "Epoch 180/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 21.5694\n",
+            "Epoch 181/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 21.5097\n",
+            "Epoch 182/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 21.4501\n",
+            "Epoch 183/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 21.3905\n",
+            "Epoch 184/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 21.3309\n",
+            "Epoch 185/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 21.2714\n",
+            "Epoch 186/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 21.2120\n",
+            "Epoch 187/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 21.1526\n",
+            "Epoch 188/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.0932\n",
+            "Epoch 189/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 21.0340\n",
+            "Epoch 190/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 20.9748\n",
+            "Epoch 191/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.9157\n",
+            "Epoch 192/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 20.8568\n",
+            "Epoch 193/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.7979\n",
+            "Epoch 194/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.7393\n",
+            "Epoch 195/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.6807\n",
+            "Epoch 196/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 20.6224\n",
+            "Epoch 197/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 20.5643\n",
+            "Epoch 198/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 20.5064\n",
+            "Epoch 199/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.4488\n",
+            "Epoch 200/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 20.3914\n",
+            "Epoch 201/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.3343\n",
+            "Epoch 202/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.2775\n",
+            "Epoch 203/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 20.2211\n",
+            "Epoch 204/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.1649\n",
+            "Epoch 205/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 20.1091\n",
+            "Epoch 206/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 20.0537\n",
+            "Epoch 207/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 19.9985\n",
+            "Epoch 208/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 19.9438\n",
+            "Epoch 209/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 19.8893\n",
+            "Epoch 210/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 19.8353\n",
+            "Epoch 211/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 19.7815\n",
+            "Epoch 212/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 19.7281\n",
+            "Epoch 213/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 19.6751\n",
+            "Epoch 214/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 19.6223\n",
+            "Epoch 215/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 19.5698\n",
+            "Epoch 216/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 19.5177\n",
+            "Epoch 217/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 19.4658\n",
+            "Epoch 218/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 19.4142\n",
+            "Epoch 219/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.3629\n",
+            "Epoch 220/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.3118\n",
+            "Epoch 221/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.2609\n",
+            "Epoch 222/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 19.2103\n",
+            "Epoch 223/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.1598\n",
+            "Epoch 224/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 19.1096\n",
+            "Epoch 225/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 19.0596\n",
+            "Epoch 226/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 19.0098\n",
+            "Epoch 227/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 18.9601\n",
+            "Epoch 228/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 18.9106\n",
+            "Epoch 229/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 18.8613\n",
+            "Epoch 230/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 18.8121\n",
+            "Epoch 231/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 18.7631\n",
+            "Epoch 232/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 18.7142\n",
+            "Epoch 233/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 18.6655\n",
+            "Epoch 234/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 18.6168\n",
+            "Epoch 235/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 18.5683\n",
+            "Epoch 236/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 18.5199\n",
+            "Epoch 237/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 18.4717\n",
+            "Epoch 238/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 18.4235\n",
+            "Epoch 239/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 18.3754\n",
+            "Epoch 240/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 18.3275\n",
+            "Epoch 241/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 18.2796\n",
+            "Epoch 242/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 18.2318\n",
+            "Epoch 243/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 18.1842\n",
+            "Epoch 244/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 18.1366\n",
+            "Epoch 245/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 18.0891\n",
+            "Epoch 246/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 18.0416\n",
+            "Epoch 247/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.9943\n",
+            "Epoch 248/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 17.9470\n",
+            "Epoch 249/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 17.8998\n",
+            "Epoch 250/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 17.8527\n",
+            "Epoch 251/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.8056\n",
+            "Epoch 252/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 17.7586\n",
+            "Epoch 253/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 17.7117\n",
+            "Epoch 254/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 17.6649\n",
+            "Epoch 255/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 17.6181\n",
+            "Epoch 256/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.5713\n",
+            "Epoch 257/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.5247\n",
+            "Epoch 258/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 17.4780\n",
+            "Epoch 259/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.4315\n",
+            "Epoch 260/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.3850\n",
+            "Epoch 261/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 17.3385\n",
+            "Epoch 262/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 17.2921\n",
+            "Epoch 263/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.2458\n",
+            "Epoch 264/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 17.1995\n",
+            "Epoch 265/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.1532\n",
+            "Epoch 266/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 17.1070\n",
+            "Epoch 267/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 17.0609\n",
+            "Epoch 268/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.0148\n",
+            "Epoch 269/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 16.9687\n",
+            "Epoch 270/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.9227\n",
+            "Epoch 271/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 16.8768\n",
+            "Epoch 272/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 16.8308\n",
+            "Epoch 273/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 16.7850\n",
+            "Epoch 274/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 16.7391\n",
+            "Epoch 275/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 16.6933\n",
+            "Epoch 276/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.6476\n",
+            "Epoch 277/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 16.6018\n",
+            "Epoch 278/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.5562\n",
+            "Epoch 279/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.5105\n",
+            "Epoch 280/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 16.4649\n",
+            "Epoch 281/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 16.4193\n",
+            "Epoch 282/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 16.3738\n",
+            "Epoch 283/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 16.3283\n",
+            "Epoch 284/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 16.2829\n",
+            "Epoch 285/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 16.2375\n",
+            "Epoch 286/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 16.1921\n",
+            "Epoch 287/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.1467\n",
+            "Epoch 288/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 16.1014\n",
+            "Epoch 289/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 16.0562\n",
+            "Epoch 290/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.0109\n",
+            "Epoch 291/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.9657\n",
+            "Epoch 292/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.9205\n",
+            "Epoch 293/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 15.8754\n",
+            "Epoch 294/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 15.8303\n",
+            "Epoch 295/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.7852\n",
+            "Epoch 296/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.7402\n",
+            "Epoch 297/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.6952\n",
+            "Epoch 298/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 15.6503\n",
+            "Epoch 299/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.6053\n",
+            "Epoch 300/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 15.5604\n",
+            "Epoch 301/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.5156\n",
+            "Epoch 302/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.4708\n",
+            "Epoch 303/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.4260\n",
+            "Epoch 304/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.3812\n",
+            "Epoch 305/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.3365\n",
+            "Epoch 306/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.2918\n",
+            "Epoch 307/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.2472\n",
+            "Epoch 308/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 15.2025\n",
+            "Epoch 309/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 15.1580\n",
+            "Epoch 310/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.1134\n",
+            "Epoch 311/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 15.0689\n",
+            "Epoch 312/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 15.0244\n",
+            "Epoch 313/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 14.9800\n",
+            "Epoch 314/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.9356\n",
+            "Epoch 315/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 14.8913\n",
+            "Epoch 316/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 14.8469\n",
+            "Epoch 317/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 14.8026\n",
+            "Epoch 318/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.7584\n",
+            "Epoch 319/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 14.7142\n",
+            "Epoch 320/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.6700\n",
+            "Epoch 321/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 14.6259\n",
+            "Epoch 322/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.5818\n",
+            "Epoch 323/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 14.5377\n",
+            "Epoch 324/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.4937\n",
+            "Epoch 325/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.4498\n",
+            "Epoch 326/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.4058\n",
+            "Epoch 327/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 14.3620\n",
+            "Epoch 328/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.3181\n",
+            "Epoch 329/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.2743\n",
+            "Epoch 330/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 14.2306\n",
+            "Epoch 331/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.1868\n",
+            "Epoch 332/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.1432\n",
+            "Epoch 333/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.0996\n",
+            "Epoch 334/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 14.0560\n",
+            "Epoch 335/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.0124\n",
+            "Epoch 336/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.9690\n",
+            "Epoch 337/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.9255\n",
+            "Epoch 338/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.8821\n",
+            "Epoch 339/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.8388\n",
+            "Epoch 340/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 13.7955\n",
+            "Epoch 341/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.7523\n",
+            "Epoch 342/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 13.7091\n",
+            "Epoch 343/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.6659\n",
+            "Epoch 344/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 13.6229\n",
+            "Epoch 345/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.5798\n",
+            "Epoch 346/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.5368\n",
+            "Epoch 347/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.4939\n",
+            "Epoch 348/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.4510\n",
+            "Epoch 349/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.4082\n",
+            "Epoch 350/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.3655\n",
+            "Epoch 351/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.3227\n",
+            "Epoch 352/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 13.2801\n",
+            "Epoch 353/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.2375\n",
+            "Epoch 354/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 13.1950\n",
+            "Epoch 355/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.1525\n",
+            "Epoch 356/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.1101\n",
+            "Epoch 357/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.0677\n",
+            "Epoch 358/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 13.0254\n",
+            "Epoch 359/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 12.9832\n",
+            "Epoch 360/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 12.9410\n",
+            "Epoch 361/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 12.8989\n",
+            "Epoch 362/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.8569\n",
+            "Epoch 363/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.8149\n",
+            "Epoch 364/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.7730\n",
+            "Epoch 365/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.7312\n",
+            "Epoch 366/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 12.6894\n",
+            "Epoch 367/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 12.6477\n",
+            "Epoch 368/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.6061\n",
+            "Epoch 369/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.5645\n",
+            "Epoch 370/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.5230\n",
+            "Epoch 371/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.4816\n",
+            "Epoch 372/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.4402\n",
+            "Epoch 373/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.3989\n",
+            "Epoch 374/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.3577\n",
+            "Epoch 375/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.3166\n",
+            "Epoch 376/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 12.2755\n",
+            "Epoch 377/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 12.2345\n",
+            "Epoch 378/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.1936\n",
+            "Epoch 379/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.1528\n",
+            "Epoch 380/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.1120\n",
+            "Epoch 381/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.0713\n",
+            "Epoch 382/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 12.0307\n",
+            "Epoch 383/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.9902\n",
+            "Epoch 384/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 11.9498\n",
+            "Epoch 385/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.9094\n",
+            "Epoch 386/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.8691\n",
+            "Epoch 387/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.8289\n",
+            "Epoch 388/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.7888\n",
+            "Epoch 389/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 11.7488\n",
+            "Epoch 390/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 11.7088\n",
+            "Epoch 391/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.6689\n",
+            "Epoch 392/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.6292\n",
+            "Epoch 393/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.5895\n",
+            "Epoch 394/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.5498\n",
+            "Epoch 395/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.5103\n",
+            "Epoch 396/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 11.4709\n",
+            "Epoch 397/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.4315\n",
+            "Epoch 398/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.3923\n",
+            "Epoch 399/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.3531\n",
+            "Epoch 400/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.3140\n",
+            "Epoch 401/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.2750\n",
+            "Epoch 402/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.2361\n",
+            "Epoch 403/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 11.1973\n",
+            "Epoch 404/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.1586\n",
+            "Epoch 405/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.1200\n",
+            "Epoch 406/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.0814\n",
+            "Epoch 407/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.0430\n",
+            "Epoch 408/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.0046\n",
+            "Epoch 409/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 10.9664\n",
+            "Epoch 410/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 10.9282\n",
+            "Epoch 411/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 10.8902\n",
+            "Epoch 412/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 10.8522\n",
+            "Epoch 413/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.8143\n",
+            "Epoch 414/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.7766\n",
+            "Epoch 415/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 10.7389\n",
+            "Epoch 416/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 10.7013\n",
+            "Epoch 417/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.6638\n",
+            "Epoch 418/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 10.6264\n",
+            "Epoch 419/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 10.5891\n",
+            "Epoch 420/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.5520\n",
+            "Epoch 421/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 10.5149\n",
+            "Epoch 422/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 10.4779\n",
+            "Epoch 423/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.4410\n",
+            "Epoch 424/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 10.4042\n",
+            "Epoch 425/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 10.3675\n",
+            "Epoch 426/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 10.3309\n",
+            "Epoch 427/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 10.2945\n",
+            "Epoch 428/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 10.2581\n",
+            "Epoch 429/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 10.2218\n",
+            "Epoch 430/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.1856\n",
+            "Epoch 431/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 10.1495\n",
+            "Epoch 432/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 10.1136\n",
+            "Epoch 433/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 10.0777\n",
+            "Epoch 434/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 10.0419\n",
+            "Epoch 435/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 10.0063\n",
+            "Epoch 436/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.9707\n",
+            "Epoch 437/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.9353\n",
+            "Epoch 438/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.8999\n",
+            "Epoch 439/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.8647\n",
+            "Epoch 440/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.8295\n",
+            "Epoch 441/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.7945\n",
+            "Epoch 442/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.7596\n",
+            "Epoch 443/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.7247\n",
+            "Epoch 444/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.6900\n",
+            "Epoch 445/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.6554\n",
+            "Epoch 446/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 9.6209\n",
+            "Epoch 447/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.5865\n",
+            "Epoch 448/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 9.5522\n",
+            "Epoch 449/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 9.5180\n",
+            "Epoch 450/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.4839\n",
+            "Epoch 451/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.4499\n",
+            "Epoch 452/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.4160\n",
+            "Epoch 453/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 9.3823\n",
+            "Epoch 454/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.3486\n",
+            "Epoch 455/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.3150\n",
+            "Epoch 456/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 9.2816\n",
+            "Epoch 457/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 9.2483\n",
+            "Epoch 458/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.2150\n",
+            "Epoch 459/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.1819\n",
+            "Epoch 460/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.1489\n",
+            "Epoch 461/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.1159\n",
+            "Epoch 462/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 9.0831\n",
+            "Epoch 463/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 9.0504\n",
+            "Epoch 464/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.0178\n",
+            "Epoch 465/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 8.9853\n",
+            "Epoch 466/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.9530\n",
+            "Epoch 467/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 8.9207\n",
+            "Epoch 468/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 8.8885\n",
+            "Epoch 469/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.8564\n",
+            "Epoch 470/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 8.8245\n",
+            "Epoch 471/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 8.7926\n",
+            "Epoch 472/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 8.7609\n",
+            "Epoch 473/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.7292\n",
+            "Epoch 474/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 8.6977\n",
+            "Epoch 475/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 8.6663\n",
+            "Epoch 476/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 8.6349\n",
+            "Epoch 477/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 8.6037\n",
+            "Epoch 478/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 8.5726\n",
+            "Epoch 479/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 8.5416\n",
+            "Epoch 480/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 8.5107\n",
+            "Epoch 481/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 8.4799\n",
+            "Epoch 482/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 8.4492\n",
+            "Epoch 483/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 8.4186\n",
+            "Epoch 484/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 8.3882\n",
+            "Epoch 485/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.3578\n",
+            "Epoch 486/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 8.3275\n",
+            "Epoch 487/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 8.2973\n",
+            "Epoch 488/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 8.2673\n",
+            "Epoch 489/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 8.2373\n",
+            "Epoch 490/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 8.2075\n",
+            "Epoch 491/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 8.1777\n",
+            "Epoch 492/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.1481\n",
+            "Epoch 493/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 8.1185\n",
+            "Epoch 494/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 8.0891\n",
+            "Epoch 495/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 8.0597\n",
+            "Epoch 496/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 8.0305\n",
+            "Epoch 497/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 8.0014\n",
+            "Epoch 498/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.9723\n",
+            "Epoch 499/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 7.9434\n",
+            "Epoch 500/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 7.9146\n",
+            "Epoch 501/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 7.8858\n",
+            "Epoch 502/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 7.8572\n",
+            "Epoch 503/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 7.8287\n",
+            "Epoch 504/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 7.8003\n",
+            "Epoch 505/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 7.7720\n",
+            "Epoch 506/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 7.7437\n",
+            "Epoch 507/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 7.7156\n",
+            "Epoch 508/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 7.6876\n",
+            "Epoch 509/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.6597\n",
+            "Epoch 510/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 7.6319\n",
+            "Epoch 511/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.6041\n",
+            "Epoch 512/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.5765\n",
+            "Epoch 513/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.5490\n",
+            "Epoch 514/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.5216\n",
+            "Epoch 515/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.4942\n",
+            "Epoch 516/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 7.4670\n",
+            "Epoch 517/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.4399\n",
+            "Epoch 518/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 7.4129\n",
+            "Epoch 519/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 7.3859\n",
+            "Epoch 520/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.3591\n",
+            "Epoch 521/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.3323\n",
+            "Epoch 522/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.3057\n",
+            "Epoch 523/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.2792\n",
+            "Epoch 524/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.2527\n",
+            "Epoch 525/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.2263\n",
+            "Epoch 526/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.2001\n",
+            "Epoch 527/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.1739\n",
+            "Epoch 528/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.1479\n",
+            "Epoch 529/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.1219\n",
+            "Epoch 530/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.0960\n",
+            "Epoch 531/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.0702\n",
+            "Epoch 532/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 7.0445\n",
+            "Epoch 533/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 7.0189\n",
+            "Epoch 534/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 6.9934\n",
+            "Epoch 535/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.9680\n",
+            "Epoch 536/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 6.9427\n",
+            "Epoch 537/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.9174\n",
+            "Epoch 538/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.8923\n",
+            "Epoch 539/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.8673\n",
+            "Epoch 540/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.8423\n",
+            "Epoch 541/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.8174\n",
+            "Epoch 542/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.7927\n",
+            "Epoch 543/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 6.7680\n",
+            "Epoch 544/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.7434\n",
+            "Epoch 545/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 6.7189\n",
+            "Epoch 546/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 6.6945\n",
+            "Epoch 547/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.6701\n",
+            "Epoch 548/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.6459\n",
+            "Epoch 549/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.6218\n",
+            "Epoch 550/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.5977\n",
+            "Epoch 551/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.5737\n",
+            "Epoch 552/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.5499\n",
+            "Epoch 553/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.5261\n",
+            "Epoch 554/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.5024\n",
+            "Epoch 555/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.4787\n",
+            "Epoch 556/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 6.4552\n",
+            "Epoch 557/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.4317\n",
+            "Epoch 558/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.4084\n",
+            "Epoch 559/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.3851\n",
+            "Epoch 560/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.3619\n",
+            "Epoch 561/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 6.3388\n",
+            "Epoch 562/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.3158\n",
+            "Epoch 563/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.2929\n",
+            "Epoch 564/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 6.2700\n",
+            "Epoch 565/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.2472\n",
+            "Epoch 566/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 6.2245\n",
+            "Epoch 567/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.2019\n",
+            "Epoch 568/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.1794\n",
+            "Epoch 569/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 6.1570\n",
+            "Epoch 570/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.1346\n",
+            "Epoch 571/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.1124\n",
+            "Epoch 572/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.0902\n",
+            "Epoch 573/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.0681\n",
+            "Epoch 574/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.0460\n",
+            "Epoch 575/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.0241\n",
+            "Epoch 576/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.0022\n",
+            "Epoch 577/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 5.9804\n",
+            "Epoch 578/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.9587\n",
+            "Epoch 579/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.9371\n",
+            "Epoch 580/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.9155\n",
+            "Epoch 581/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.8941\n",
+            "Epoch 582/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.8727\n",
+            "Epoch 583/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.8514\n",
+            "Epoch 584/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.8301\n",
+            "Epoch 585/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.8090\n",
+            "Epoch 586/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.7879\n",
+            "Epoch 587/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.7669\n",
+            "Epoch 588/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.7460\n",
+            "Epoch 589/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.7251\n",
+            "Epoch 590/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.7043\n",
+            "Epoch 591/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.6836\n",
+            "Epoch 592/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.6630\n",
+            "Epoch 593/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.6425\n",
+            "Epoch 594/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.6220\n",
+            "Epoch 595/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.6016\n",
+            "Epoch 596/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 5.5813\n",
+            "Epoch 597/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 5.5610\n",
+            "Epoch 598/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.5409\n",
+            "Epoch 599/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.5208\n",
+            "Epoch 600/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.5007\n",
+            "Epoch 601/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.4808\n",
+            "Epoch 602/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.4609\n",
+            "Epoch 603/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.4411\n",
+            "Epoch 604/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.4214\n",
+            "Epoch 605/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.4017\n",
+            "Epoch 606/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.3821\n",
+            "Epoch 607/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.3626\n",
+            "Epoch 608/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.3431\n",
+            "Epoch 609/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.3237\n",
+            "Epoch 610/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.3044\n",
+            "Epoch 611/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.2852\n",
+            "Epoch 612/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.2660\n",
+            "Epoch 613/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.2469\n",
+            "Epoch 614/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.2279\n",
+            "Epoch 615/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.2089\n",
+            "Epoch 616/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.1900\n",
+            "Epoch 617/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.1712\n",
+            "Epoch 618/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 5.1525\n",
+            "Epoch 619/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.1338\n",
+            "Epoch 620/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 5.1152\n",
+            "Epoch 621/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.0966\n",
+            "Epoch 622/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.0781\n",
+            "Epoch 623/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.0597\n",
+            "Epoch 624/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.0414\n",
+            "Epoch 625/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.0231\n",
+            "Epoch 626/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 5.0049\n",
+            "Epoch 627/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.9867\n",
+            "Epoch 628/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.9686\n",
+            "Epoch 629/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.9506\n",
+            "Epoch 630/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.9327\n",
+            "Epoch 631/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.9148\n",
+            "Epoch 632/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.8969\n",
+            "Epoch 633/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.8792\n",
+            "Epoch 634/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.8615\n",
+            "Epoch 635/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.8438\n",
+            "Epoch 636/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.8263\n",
+            "Epoch 637/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.8088\n",
+            "Epoch 638/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 4.7913\n",
+            "Epoch 639/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.7739\n",
+            "Epoch 640/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.7566\n",
+            "Epoch 641/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 4.7394\n",
+            "Epoch 642/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.7222\n",
+            "Epoch 643/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.7051\n",
+            "Epoch 644/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.6880\n",
+            "Epoch 645/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.6710\n",
+            "Epoch 646/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.6540\n",
+            "Epoch 647/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.6371\n",
+            "Epoch 648/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.6203\n",
+            "Epoch 649/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.6036\n",
+            "Epoch 650/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.5868\n",
+            "Epoch 651/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.5702\n",
+            "Epoch 652/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.5536\n",
+            "Epoch 653/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.5371\n",
+            "Epoch 654/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.5206\n",
+            "Epoch 655/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.5042\n",
+            "Epoch 656/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.4879\n",
+            "Epoch 657/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.4716\n",
+            "Epoch 658/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.4554\n",
+            "Epoch 659/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.4392\n",
+            "Epoch 660/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.4231\n",
+            "Epoch 661/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.4070\n",
+            "Epoch 662/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.3910\n",
+            "Epoch 663/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.3751\n",
+            "Epoch 664/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 4.3592\n",
+            "Epoch 665/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.3433\n",
+            "Epoch 666/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 4.3276\n",
+            "Epoch 667/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.3119\n",
+            "Epoch 668/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.2962\n",
+            "Epoch 669/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.2806\n",
+            "Epoch 670/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.2650\n",
+            "Epoch 671/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.2496\n",
+            "Epoch 672/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.2341\n",
+            "Epoch 673/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.2187\n",
+            "Epoch 674/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.2034\n",
+            "Epoch 675/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.1881\n",
+            "Epoch 676/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.1729\n",
+            "Epoch 677/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.1578\n",
+            "Epoch 678/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.1426\n",
+            "Epoch 679/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.1276\n",
+            "Epoch 680/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 4.1126\n",
+            "Epoch 681/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.0976\n",
+            "Epoch 682/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0827\n",
+            "Epoch 683/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.0679\n",
+            "Epoch 684/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0531\n",
+            "Epoch 685/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0384\n",
+            "Epoch 686/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.0237\n",
+            "Epoch 687/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.0090\n",
+            "Epoch 688/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.9945\n",
+            "Epoch 689/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.9799\n",
+            "Epoch 690/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.9654\n",
+            "Epoch 691/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 3.9510\n",
+            "Epoch 692/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.9366\n",
+            "Epoch 693/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.9223\n",
+            "Epoch 694/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.9080\n",
+            "Epoch 695/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.8938\n",
+            "Epoch 696/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.8796\n",
+            "Epoch 697/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.8655\n",
+            "Epoch 698/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.8514\n",
+            "Epoch 699/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.8374\n",
+            "Epoch 700/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.8234\n",
+            "Epoch 701/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.8095\n",
+            "Epoch 702/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.7956\n",
+            "Epoch 703/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.7818\n",
+            "Epoch 704/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.7680\n",
+            "Epoch 705/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 3.7543\n",
+            "Epoch 706/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 3.7406\n",
+            "Epoch 707/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 3.7270\n",
+            "Epoch 708/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.7134\n",
+            "Epoch 709/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 3.6998\n",
+            "Epoch 710/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 3.6864\n",
+            "Epoch 711/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 3.6729\n",
+            "Epoch 712/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 3.6595\n",
+            "Epoch 713/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 3.6462\n",
+            "Epoch 714/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.6329\n",
+            "Epoch 715/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.6196\n",
+            "Epoch 716/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 3.6064\n",
+            "Epoch 717/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.5932\n",
+            "Epoch 718/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.5801\n",
+            "Epoch 719/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 3.5671\n",
+            "Epoch 720/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 3.5540\n",
+            "Epoch 721/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 3.5411\n",
+            "Epoch 722/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 3.5281\n",
+            "Epoch 723/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 3.5152\n",
+            "Epoch 724/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.5024\n",
+            "Epoch 725/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 3.4896\n",
+            "Epoch 726/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 3.4769\n",
+            "Epoch 727/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 3.4641\n",
+            "Epoch 728/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 3.4515\n",
+            "Epoch 729/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.4389\n",
+            "Epoch 730/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.4263\n",
+            "Epoch 731/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 3.4138\n",
+            "Epoch 732/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 3.4013\n",
+            "Epoch 733/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 3.3888\n",
+            "Epoch 734/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 3.3764\n",
+            "Epoch 735/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 3.3641\n",
+            "Epoch 736/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 3.3518\n",
+            "Epoch 737/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 3.3395\n",
+            "Epoch 738/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.3273\n",
+            "Epoch 739/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 3.3151\n",
+            "Epoch 740/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 3.3029\n",
+            "Epoch 741/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 3.2908\n",
+            "Epoch 742/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 3.2788\n",
+            "Epoch 743/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.2668\n",
+            "Epoch 744/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.2548\n",
+            "Epoch 745/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.2429\n",
+            "Epoch 746/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.2310\n",
+            "Epoch 747/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.2191\n",
+            "Epoch 748/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 3.2073\n",
+            "Epoch 749/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.1956\n",
+            "Epoch 750/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.1838\n",
+            "Epoch 751/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.1722\n",
+            "Epoch 752/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.1605\n",
+            "Epoch 753/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 3.1489\n",
+            "Epoch 754/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.1374\n",
+            "Epoch 755/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.1258\n",
+            "Epoch 756/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.1144\n",
+            "Epoch 757/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.1029\n",
+            "Epoch 758/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.0915\n",
+            "Epoch 759/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.0802\n",
+            "Epoch 760/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.0688\n",
+            "Epoch 761/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.0576\n",
+            "Epoch 762/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.0463\n",
+            "Epoch 763/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.0351\n",
+            "Epoch 764/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 3.0239\n",
+            "Epoch 765/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.0128\n",
+            "Epoch 766/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.0017\n",
+            "Epoch 767/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.9907\n",
+            "Epoch 768/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.9797\n",
+            "Epoch 769/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.9687\n",
+            "Epoch 770/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.9578\n",
+            "Epoch 771/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.9469\n",
+            "Epoch 772/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.9360\n",
+            "Epoch 773/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.9252\n",
+            "Epoch 774/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.9144\n",
+            "Epoch 775/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.9037\n",
+            "Epoch 776/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.8930\n",
+            "Epoch 777/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.8823\n",
+            "Epoch 778/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.8717\n",
+            "Epoch 779/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.8611\n",
+            "Epoch 780/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.8505\n",
+            "Epoch 781/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.8400\n",
+            "Epoch 782/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.8295\n",
+            "Epoch 783/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.8191\n",
+            "Epoch 784/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.8086\n",
+            "Epoch 785/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7983\n",
+            "Epoch 786/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.7879\n",
+            "Epoch 787/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.7776\n",
+            "Epoch 788/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7673\n",
+            "Epoch 789/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7571\n",
+            "Epoch 790/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7469\n",
+            "Epoch 791/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7368\n",
+            "Epoch 792/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.7266\n",
+            "Epoch 793/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.7165\n",
+            "Epoch 794/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7065\n",
+            "Epoch 795/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6965\n",
+            "Epoch 796/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6865\n",
+            "Epoch 797/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.6765\n",
+            "Epoch 798/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.6666\n",
+            "Epoch 799/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.6567\n",
+            "Epoch 800/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.6469\n",
+            "Epoch 801/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6370\n",
+            "Epoch 802/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6273\n",
+            "Epoch 803/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.6175\n",
+            "Epoch 804/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.6078\n",
+            "Epoch 805/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 2.5981\n",
+            "Epoch 806/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.5885\n",
+            "Epoch 807/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.5789\n",
+            "Epoch 808/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.5693\n",
+            "Epoch 809/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.5597\n",
+            "Epoch 810/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.5502\n",
+            "Epoch 811/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.5407\n",
+            "Epoch 812/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.5313\n",
+            "Epoch 813/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.5219\n",
+            "Epoch 814/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.5125\n",
+            "Epoch 815/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.5031\n",
+            "Epoch 816/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4938\n",
+            "Epoch 817/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.4845\n",
+            "Epoch 818/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4753\n",
+            "Epoch 819/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.4661\n",
+            "Epoch 820/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.4569\n",
+            "Epoch 821/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4477\n",
+            "Epoch 822/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.4386\n",
+            "Epoch 823/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4295\n",
+            "Epoch 824/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4204\n",
+            "Epoch 825/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.4114\n",
+            "Epoch 826/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4024\n",
+            "Epoch 827/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3934\n",
+            "Epoch 828/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.3845\n",
+            "Epoch 829/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3756\n",
+            "Epoch 830/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3667\n",
+            "Epoch 831/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3579\n",
+            "Epoch 832/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3491\n",
+            "Epoch 833/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.3403\n",
+            "Epoch 834/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 2.3315\n",
+            "Epoch 835/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.3228\n",
+            "Epoch 836/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.3141\n",
+            "Epoch 837/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.3054\n",
+            "Epoch 838/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.2968\n",
+            "Epoch 839/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.2882\n",
+            "Epoch 840/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.2796\n",
+            "Epoch 841/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.2711\n",
+            "Epoch 842/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.2626\n",
+            "Epoch 843/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.2541\n",
+            "Epoch 844/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.2456\n",
+            "Epoch 845/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.2372\n",
+            "Epoch 846/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.2288\n",
+            "Epoch 847/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.2205\n",
+            "Epoch 848/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.2121\n",
+            "Epoch 849/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.2038\n",
+            "Epoch 850/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.1955\n",
+            "Epoch 851/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.1873\n",
+            "Epoch 852/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.1791\n",
+            "Epoch 853/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1709\n",
+            "Epoch 854/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 2.1627\n",
+            "Epoch 855/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1546\n",
+            "Epoch 856/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.1464\n",
+            "Epoch 857/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1384\n",
+            "Epoch 858/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.1303\n",
+            "Epoch 859/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1223\n",
+            "Epoch 860/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1143\n",
+            "Epoch 861/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.1063\n",
+            "Epoch 862/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0984\n",
+            "Epoch 863/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0905\n",
+            "Epoch 864/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.0826\n",
+            "Epoch 865/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0747\n",
+            "Epoch 866/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.0669\n",
+            "Epoch 867/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0591\n",
+            "Epoch 868/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0513\n",
+            "Epoch 869/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.0435\n",
+            "Epoch 870/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0358\n",
+            "Epoch 871/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.0281\n",
+            "Epoch 872/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0205\n",
+            "Epoch 873/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0128\n",
+            "Epoch 874/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.0052\n",
+            "Epoch 875/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.9976\n",
+            "Epoch 876/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9900\n",
+            "Epoch 877/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.9825\n",
+            "Epoch 878/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.9750\n",
+            "Epoch 879/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.9675\n",
+            "Epoch 880/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9600\n",
+            "Epoch 881/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.9526\n",
+            "Epoch 882/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.9452\n",
+            "Epoch 883/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.9378\n",
+            "Epoch 884/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.9305\n",
+            "Epoch 885/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.9231\n",
+            "Epoch 886/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9158\n",
+            "Epoch 887/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9085\n",
+            "Epoch 888/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.9013\n",
+            "Epoch 889/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.8941\n",
+            "Epoch 890/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.8869\n",
+            "Epoch 891/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.8797\n",
+            "Epoch 892/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.8725\n",
+            "Epoch 893/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.8654\n",
+            "Epoch 894/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.8583\n",
+            "Epoch 895/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.8512\n",
+            "Epoch 896/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.8441\n",
+            "Epoch 897/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.8371\n",
+            "Epoch 898/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.8301\n",
+            "Epoch 899/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.8231\n",
+            "Epoch 900/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.8162\n",
+            "Epoch 901/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.8092\n",
+            "Epoch 902/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.8023\n",
+            "Epoch 903/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.7954\n",
+            "Epoch 904/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7886\n",
+            "Epoch 905/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.7817\n",
+            "Epoch 906/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7749\n",
+            "Epoch 907/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.7681\n",
+            "Epoch 908/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7614\n",
+            "Epoch 909/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.7546\n",
+            "Epoch 910/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.7479\n",
+            "Epoch 911/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.7412\n",
+            "Epoch 912/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.7346\n",
+            "Epoch 913/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.7279\n",
+            "Epoch 914/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.7213\n",
+            "Epoch 915/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.7147\n",
+            "Epoch 916/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.7081\n",
+            "Epoch 917/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.7015\n",
+            "Epoch 918/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6950\n",
+            "Epoch 919/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.6885\n",
+            "Epoch 920/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.6820\n",
+            "Epoch 921/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.6755\n",
+            "Epoch 922/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.6691\n",
+            "Epoch 923/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6627\n",
+            "Epoch 924/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.6563\n",
+            "Epoch 925/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.6499\n",
+            "Epoch 926/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6436\n",
+            "Epoch 927/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6372\n",
+            "Epoch 928/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6309\n",
+            "Epoch 929/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.6246\n",
+            "Epoch 930/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 1.6184\n",
+            "Epoch 931/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.6121\n",
+            "Epoch 932/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.6059\n",
+            "Epoch 933/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.5997\n",
+            "Epoch 934/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.5935\n",
+            "Epoch 935/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.5874\n",
+            "Epoch 936/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.5813\n",
+            "Epoch 937/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.5751\n",
+            "Epoch 938/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.5691\n",
+            "Epoch 939/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 1.5630\n",
+            "Epoch 940/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 1.5569\n",
+            "Epoch 941/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.5509\n",
+            "Epoch 942/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 1.5449\n",
+            "Epoch 943/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.5389\n",
+            "Epoch 944/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 1.5330\n",
+            "Epoch 945/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 1.5270\n",
+            "Epoch 946/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.5211\n",
+            "Epoch 947/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.5152\n",
+            "Epoch 948/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.5093\n",
+            "Epoch 949/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.5035\n",
+            "Epoch 950/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.4976\n",
+            "Epoch 951/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.4918\n",
+            "Epoch 952/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 1.4860\n",
+            "Epoch 953/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.4802\n",
+            "Epoch 954/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 1.4745\n",
+            "Epoch 955/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.4687\n",
+            "Epoch 956/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.4630\n",
+            "Epoch 957/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.4573\n",
+            "Epoch 958/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 1.4517\n",
+            "Epoch 959/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 1.4460\n",
+            "Epoch 960/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.4404\n",
+            "Epoch 961/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 1.4348\n",
+            "Epoch 962/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.4292\n",
+            "Epoch 963/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 1.4236\n",
+            "Epoch 964/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 1.4180\n",
+            "Epoch 965/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.4125\n",
+            "Epoch 966/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.4070\n",
+            "Epoch 967/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.4015\n",
+            "Epoch 968/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.3960\n",
+            "Epoch 969/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.3905\n",
+            "Epoch 970/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.3851\n",
+            "Epoch 971/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.3797\n",
+            "Epoch 972/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.3743\n",
+            "Epoch 973/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.3689\n",
+            "Epoch 974/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.3635\n",
+            "Epoch 975/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.3582\n",
+            "Epoch 976/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.3529\n",
+            "Epoch 977/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.3476\n",
+            "Epoch 978/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.3423\n",
+            "Epoch 979/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.3370\n",
+            "Epoch 980/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.3318\n",
+            "Epoch 981/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 1.3265\n",
+            "Epoch 982/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.3213\n",
+            "Epoch 983/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.3161\n",
+            "Epoch 984/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.3110\n",
+            "Epoch 985/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.3058\n",
+            "Epoch 986/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.3007\n",
+            "Epoch 987/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.2955\n",
+            "Epoch 988/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.2904\n",
+            "Epoch 989/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.2854\n",
+            "Epoch 990/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2803\n",
+            "Epoch 991/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.2752\n",
+            "Epoch 992/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.2702\n",
+            "Epoch 993/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2652\n",
+            "Epoch 994/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.2602\n",
+            "Epoch 995/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.2552\n",
+            "Epoch 996/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.2503\n",
+            "Epoch 997/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.2453\n",
+            "Epoch 998/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2404\n",
+            "Epoch 999/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.2355\n",
+            "Epoch 1000/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 1.2306\n",
+            "Epoch 1000/3000\n",
+            " - loss: 1.2306\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.2306\n",
+            "Epoch 1001/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.2258\n",
+            "Epoch 1002/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.2209\n",
+            "Epoch 1003/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.2161\n",
+            "Epoch 1004/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.2113\n",
+            "Epoch 1005/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.2064\n",
+            "Epoch 1006/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.2017\n",
+            "Epoch 1007/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.1969\n",
+            "Epoch 1008/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.1922\n",
+            "Epoch 1009/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.1874\n",
+            "Epoch 1010/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.1827\n",
+            "Epoch 1011/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.1780\n",
+            "Epoch 1012/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.1733\n",
+            "Epoch 1013/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.1687\n",
+            "Epoch 1014/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.1640\n",
+            "Epoch 1015/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.1594\n",
+            "Epoch 1016/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.1548\n",
+            "Epoch 1017/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.1502\n",
+            "Epoch 1018/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.1456\n",
+            "Epoch 1019/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.1410\n",
+            "Epoch 1020/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.1365\n",
+            "Epoch 1021/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.1319\n",
+            "Epoch 1022/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.1274\n",
+            "Epoch 1023/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.1229\n",
+            "Epoch 1024/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.1184\n",
+            "Epoch 1025/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.1140\n",
+            "Epoch 1026/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.1095\n",
+            "Epoch 1027/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.1051\n",
+            "Epoch 1028/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.1007\n",
+            "Epoch 1029/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0963\n",
+            "Epoch 1030/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.0919\n",
+            "Epoch 1031/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0875\n",
+            "Epoch 1032/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.0832\n",
+            "Epoch 1033/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.0788\n",
+            "Epoch 1034/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.0745\n",
+            "Epoch 1035/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.0702\n",
+            "Epoch 1036/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.0659\n",
+            "Epoch 1037/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.0616\n",
+            "Epoch 1038/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 1.0573\n",
+            "Epoch 1039/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.0531\n",
+            "Epoch 1040/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.0489\n",
+            "Epoch 1041/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0447\n",
+            "Epoch 1042/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.0405\n",
+            "Epoch 1043/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.0363\n",
+            "Epoch 1044/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.0321\n",
+            "Epoch 1045/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.0279\n",
+            "Epoch 1046/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0238\n",
+            "Epoch 1047/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.0197\n",
+            "Epoch 1048/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.0156\n",
+            "Epoch 1049/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.0115\n",
+            "Epoch 1050/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.0074\n",
+            "Epoch 1051/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.0033\n",
+            "Epoch 1052/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.9993\n",
+            "Epoch 1053/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.9952\n",
+            "Epoch 1054/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.9912\n",
+            "Epoch 1055/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.9872\n",
+            "Epoch 1056/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.9832\n",
+            "Epoch 1057/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.9792\n",
+            "Epoch 1058/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.9753\n",
+            "Epoch 1059/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.9713\n",
+            "Epoch 1060/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.9674\n",
+            "Epoch 1061/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.9635\n",
+            "Epoch 1062/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.9596\n",
+            "Epoch 1063/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.9557\n",
+            "Epoch 1064/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.9518\n",
+            "Epoch 1065/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.9480\n",
+            "Epoch 1066/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.9441\n",
+            "Epoch 1067/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.9403\n",
+            "Epoch 1068/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.9364\n",
+            "Epoch 1069/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.9326\n",
+            "Epoch 1070/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.9289\n",
+            "Epoch 1071/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.9251\n",
+            "Epoch 1072/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.9213\n",
+            "Epoch 1073/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.9176\n",
+            "Epoch 1074/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.9138\n",
+            "Epoch 1075/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.9101\n",
+            "Epoch 1076/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.9064\n",
+            "Epoch 1077/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.9027\n",
+            "Epoch 1078/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.8990\n",
+            "Epoch 1079/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.8953\n",
+            "Epoch 1080/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.8917\n",
+            "Epoch 1081/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8880\n",
+            "Epoch 1082/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.8844\n",
+            "Epoch 1083/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.8808\n",
+            "Epoch 1084/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.8772\n",
+            "Epoch 1085/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8736\n",
+            "Epoch 1086/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.8700\n",
+            "Epoch 1087/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.8665\n",
+            "Epoch 1088/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8629\n",
+            "Epoch 1089/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.8594\n",
+            "Epoch 1090/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.8559\n",
+            "Epoch 1091/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.8524\n",
+            "Epoch 1092/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.8489\n",
+            "Epoch 1093/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8454\n",
+            "Epoch 1094/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.8419\n",
+            "Epoch 1095/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.8385\n",
+            "Epoch 1096/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.8350\n",
+            "Epoch 1097/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8316\n",
+            "Epoch 1098/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.8282\n",
+            "Epoch 1099/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8247\n",
+            "Epoch 1100/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8214\n",
+            "Epoch 1101/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.8180\n",
+            "Epoch 1102/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.8146\n",
+            "Epoch 1103/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.8112\n",
+            "Epoch 1104/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8079\n",
+            "Epoch 1105/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.8046\n",
+            "Epoch 1106/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.8013\n",
+            "Epoch 1107/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7979\n",
+            "Epoch 1108/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7947\n",
+            "Epoch 1109/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.7914\n",
+            "Epoch 1110/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.7881\n",
+            "Epoch 1111/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7848\n",
+            "Epoch 1112/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7816\n",
+            "Epoch 1113/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7784\n",
+            "Epoch 1114/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.7751\n",
+            "Epoch 1115/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7719\n",
+            "Epoch 1116/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.7687\n",
+            "Epoch 1117/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7655\n",
+            "Epoch 1118/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7624\n",
+            "Epoch 1119/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7592\n",
+            "Epoch 1120/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7561\n",
+            "Epoch 1121/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7529\n",
+            "Epoch 1122/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.7498\n",
+            "Epoch 1123/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7467\n",
+            "Epoch 1124/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.7436\n",
+            "Epoch 1125/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7405\n",
+            "Epoch 1126/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.7374\n",
+            "Epoch 1127/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.7343\n",
+            "Epoch 1128/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7313\n",
+            "Epoch 1129/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.7282\n",
+            "Epoch 1130/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7252\n",
+            "Epoch 1131/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7222\n",
+            "Epoch 1132/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7192\n",
+            "Epoch 1133/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.7162\n",
+            "Epoch 1134/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7132\n",
+            "Epoch 1135/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7102\n",
+            "Epoch 1136/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7072\n",
+            "Epoch 1137/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.7043\n",
+            "Epoch 1138/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.7013\n",
+            "Epoch 1139/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.6984\n",
+            "Epoch 1140/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.6955\n",
+            "Epoch 1141/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.6926\n",
+            "Epoch 1142/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6897\n",
+            "Epoch 1143/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.6868\n",
+            "Epoch 1144/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6839\n",
+            "Epoch 1145/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.6810\n",
+            "Epoch 1146/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6782\n",
+            "Epoch 1147/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.6753\n",
+            "Epoch 1148/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.6725\n",
+            "Epoch 1149/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.6697\n",
+            "Epoch 1150/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.6669\n",
+            "Epoch 1151/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.6641\n",
+            "Epoch 1152/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.6613\n",
+            "Epoch 1153/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.6585\n",
+            "Epoch 1154/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6557\n",
+            "Epoch 1155/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.6530\n",
+            "Epoch 1156/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.6502\n",
+            "Epoch 1157/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.6475\n",
+            "Epoch 1158/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6448\n",
+            "Epoch 1159/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.6420\n",
+            "Epoch 1160/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.6393\n",
+            "Epoch 1161/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.6366\n",
+            "Epoch 1162/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.6340\n",
+            "Epoch 1163/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6313\n",
+            "Epoch 1164/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.6286\n",
+            "Epoch 1165/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.6260\n",
+            "Epoch 1166/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6233\n",
+            "Epoch 1167/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.6207\n",
+            "Epoch 1168/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.6180\n",
+            "Epoch 1169/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.6154\n",
+            "Epoch 1170/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.6128\n",
+            "Epoch 1171/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.6102\n",
+            "Epoch 1172/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.6076\n",
+            "Epoch 1173/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6051\n",
+            "Epoch 1174/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.6025\n",
+            "Epoch 1175/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.5999\n",
+            "Epoch 1176/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.5974\n",
+            "Epoch 1177/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.5949\n",
+            "Epoch 1178/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.5923\n",
+            "Epoch 1179/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5898\n",
+            "Epoch 1180/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5873\n",
+            "Epoch 1181/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5848\n",
+            "Epoch 1182/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5823\n",
+            "Epoch 1183/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5798\n",
+            "Epoch 1184/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5774\n",
+            "Epoch 1185/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5749\n",
+            "Epoch 1186/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5725\n",
+            "Epoch 1187/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5700\n",
+            "Epoch 1188/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5676\n",
+            "Epoch 1189/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5652\n",
+            "Epoch 1190/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5628\n",
+            "Epoch 1191/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5604\n",
+            "Epoch 1192/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5580\n",
+            "Epoch 1193/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5556\n",
+            "Epoch 1194/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5532\n",
+            "Epoch 1195/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.5508\n",
+            "Epoch 1196/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5485\n",
+            "Epoch 1197/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.5461\n",
+            "Epoch 1198/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5438\n",
+            "Epoch 1199/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5415\n",
+            "Epoch 1200/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5391\n",
+            "Epoch 1201/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5368\n",
+            "Epoch 1202/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5345\n",
+            "Epoch 1203/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5322\n",
+            "Epoch 1204/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5300\n",
+            "Epoch 1205/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5277\n",
+            "Epoch 1206/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5254\n",
+            "Epoch 1207/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5232\n",
+            "Epoch 1208/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5209\n",
+            "Epoch 1209/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5187\n",
+            "Epoch 1210/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5164\n",
+            "Epoch 1211/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5142\n",
+            "Epoch 1212/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.5120\n",
+            "Epoch 1213/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5098\n",
+            "Epoch 1214/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5076\n",
+            "Epoch 1215/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5054\n",
+            "Epoch 1216/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5032\n",
+            "Epoch 1217/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5011\n",
+            "Epoch 1218/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4989\n",
+            "Epoch 1219/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4967\n",
+            "Epoch 1220/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.4946\n",
+            "Epoch 1221/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4925\n",
+            "Epoch 1222/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4903\n",
+            "Epoch 1223/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4882\n",
+            "Epoch 1224/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4861\n",
+            "Epoch 1225/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4840\n",
+            "Epoch 1226/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.4819\n",
+            "Epoch 1227/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4798\n",
+            "Epoch 1228/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4777\n",
+            "Epoch 1229/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.4757\n",
+            "Epoch 1230/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.4736\n",
+            "Epoch 1231/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.4715\n",
+            "Epoch 1232/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4695\n",
+            "Epoch 1233/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4675\n",
+            "Epoch 1234/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.4654\n",
+            "Epoch 1235/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4634\n",
+            "Epoch 1236/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4614\n",
+            "Epoch 1237/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4594\n",
+            "Epoch 1238/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4574\n",
+            "Epoch 1239/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4554\n",
+            "Epoch 1240/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4534\n",
+            "Epoch 1241/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4514\n",
+            "Epoch 1242/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4495\n",
+            "Epoch 1243/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4475\n",
+            "Epoch 1244/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4456\n",
+            "Epoch 1245/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4436\n",
+            "Epoch 1246/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.4417\n",
+            "Epoch 1247/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4398\n",
+            "Epoch 1248/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4378\n",
+            "Epoch 1249/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.4359\n",
+            "Epoch 1250/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4340\n",
+            "Epoch 1251/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4321\n",
+            "Epoch 1252/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4302\n",
+            "Epoch 1253/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4283\n",
+            "Epoch 1254/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.4265\n",
+            "Epoch 1255/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4246\n",
+            "Epoch 1256/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4227\n",
+            "Epoch 1257/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4209\n",
+            "Epoch 1258/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4190\n",
+            "Epoch 1259/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4172\n",
+            "Epoch 1260/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4154\n",
+            "Epoch 1261/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4136\n",
+            "Epoch 1262/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.4117\n",
+            "Epoch 1263/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4099\n",
+            "Epoch 1264/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.4081\n",
+            "Epoch 1265/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4063\n",
+            "Epoch 1266/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4045\n",
+            "Epoch 1267/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4028\n",
+            "Epoch 1268/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4010\n",
+            "Epoch 1269/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3992\n",
+            "Epoch 1270/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3975\n",
+            "Epoch 1271/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3957\n",
+            "Epoch 1272/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3940\n",
+            "Epoch 1273/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3922\n",
+            "Epoch 1274/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3905\n",
+            "Epoch 1275/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3888\n",
+            "Epoch 1276/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3871\n",
+            "Epoch 1277/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3854\n",
+            "Epoch 1278/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3837\n",
+            "Epoch 1279/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3820\n",
+            "Epoch 1280/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3803\n",
+            "Epoch 1281/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.3786\n",
+            "Epoch 1282/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3769\n",
+            "Epoch 1283/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3753\n",
+            "Epoch 1284/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3736\n",
+            "Epoch 1285/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3719\n",
+            "Epoch 1286/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3703\n",
+            "Epoch 1287/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3686\n",
+            "Epoch 1288/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3670\n",
+            "Epoch 1289/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3654\n",
+            "Epoch 1290/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3638\n",
+            "Epoch 1291/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3621\n",
+            "Epoch 1292/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3605\n",
+            "Epoch 1293/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3589\n",
+            "Epoch 1294/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3573\n",
+            "Epoch 1295/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3558\n",
+            "Epoch 1296/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3542\n",
+            "Epoch 1297/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3526\n",
+            "Epoch 1298/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3510\n",
+            "Epoch 1299/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3495\n",
+            "Epoch 1300/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.3479\n",
+            "Epoch 1301/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.3464\n",
+            "Epoch 1302/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3448\n",
+            "Epoch 1303/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3433\n",
+            "Epoch 1304/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3417\n",
+            "Epoch 1305/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.3402\n",
+            "Epoch 1306/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3387\n",
+            "Epoch 1307/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3372\n",
+            "Epoch 1308/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3357\n",
+            "Epoch 1309/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3342\n",
+            "Epoch 1310/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3327\n",
+            "Epoch 1311/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3312\n",
+            "Epoch 1312/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3297\n",
+            "Epoch 1313/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3283\n",
+            "Epoch 1314/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.3268\n",
+            "Epoch 1315/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3253\n",
+            "Epoch 1316/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3239\n",
+            "Epoch 1317/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.3224\n",
+            "Epoch 1318/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3210\n",
+            "Epoch 1319/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3195\n",
+            "Epoch 1320/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3181\n",
+            "Epoch 1321/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.3167\n",
+            "Epoch 1322/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3152\n",
+            "Epoch 1323/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3138\n",
+            "Epoch 1324/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3124\n",
+            "Epoch 1325/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3110\n",
+            "Epoch 1326/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.3096\n",
+            "Epoch 1327/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3082\n",
+            "Epoch 1328/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3068\n",
+            "Epoch 1329/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3055\n",
+            "Epoch 1330/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3041\n",
+            "Epoch 1331/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3027\n",
+            "Epoch 1332/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3013\n",
+            "Epoch 1333/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3000\n",
+            "Epoch 1334/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2986\n",
+            "Epoch 1335/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2973\n",
+            "Epoch 1336/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2960\n",
+            "Epoch 1337/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2946\n",
+            "Epoch 1338/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2933\n",
+            "Epoch 1339/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2920\n",
+            "Epoch 1340/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2906\n",
+            "Epoch 1341/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2893\n",
+            "Epoch 1342/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.2880\n",
+            "Epoch 1343/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.2867\n",
+            "Epoch 1344/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.2854\n",
+            "Epoch 1345/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.2841\n",
+            "Epoch 1346/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2828\n",
+            "Epoch 1347/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.2816\n",
+            "Epoch 1348/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.2803\n",
+            "Epoch 1349/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.2790\n",
+            "Epoch 1350/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.2778\n",
+            "Epoch 1351/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.2765\n",
+            "Epoch 1352/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.2752\n",
+            "Epoch 1353/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.2740\n",
+            "Epoch 1354/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.2728\n",
+            "Epoch 1355/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.2715\n",
+            "Epoch 1356/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.2703\n",
+            "Epoch 1357/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.2691\n",
+            "Epoch 1358/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.2678\n",
+            "Epoch 1359/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.2666\n",
+            "Epoch 1360/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.2654\n",
+            "Epoch 1361/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.2642\n",
+            "Epoch 1362/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.2630\n",
+            "Epoch 1363/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.2618\n",
+            "Epoch 1364/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2606\n",
+            "Epoch 1365/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2594\n",
+            "Epoch 1366/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.2582\n",
+            "Epoch 1367/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.2571\n",
+            "Epoch 1368/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.2559\n",
+            "Epoch 1369/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.2547\n",
+            "Epoch 1370/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2536\n",
+            "Epoch 1371/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.2524\n",
+            "Epoch 1372/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.2512\n",
+            "Epoch 1373/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.2501\n",
+            "Epoch 1374/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2490\n",
+            "Epoch 1375/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2478\n",
+            "Epoch 1376/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.2467\n",
+            "Epoch 1377/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.2456\n",
+            "Epoch 1378/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2444\n",
+            "Epoch 1379/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.2433\n",
+            "Epoch 1380/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.2422\n",
+            "Epoch 1381/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2411\n",
+            "Epoch 1382/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2400\n",
+            "Epoch 1383/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2389\n",
+            "Epoch 1384/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2378\n",
+            "Epoch 1385/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2367\n",
+            "Epoch 1386/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2356\n",
+            "Epoch 1387/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.2345\n",
+            "Epoch 1388/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2335\n",
+            "Epoch 1389/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2324\n",
+            "Epoch 1390/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2313\n",
+            "Epoch 1391/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2303\n",
+            "Epoch 1392/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2292\n",
+            "Epoch 1393/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2282\n",
+            "Epoch 1394/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2271\n",
+            "Epoch 1395/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.2261\n",
+            "Epoch 1396/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.2250\n",
+            "Epoch 1397/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.2240\n",
+            "Epoch 1398/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.2230\n",
+            "Epoch 1399/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2219\n",
+            "Epoch 1400/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2209\n",
+            "Epoch 1401/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2199\n",
+            "Epoch 1402/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.2189\n",
+            "Epoch 1403/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2179\n",
+            "Epoch 1404/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2169\n",
+            "Epoch 1405/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2159\n",
+            "Epoch 1406/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2149\n",
+            "Epoch 1407/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.2139\n",
+            "Epoch 1408/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.2129\n",
+            "Epoch 1409/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2119\n",
+            "Epoch 1410/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.2109\n",
+            "Epoch 1411/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.2100\n",
+            "Epoch 1412/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.2090\n",
+            "Epoch 1413/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2080\n",
+            "Epoch 1414/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.2071\n",
+            "Epoch 1415/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2061\n",
+            "Epoch 1416/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.2052\n",
+            "Epoch 1417/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2042\n",
+            "Epoch 1418/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2033\n",
+            "Epoch 1419/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2023\n",
+            "Epoch 1420/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2014\n",
+            "Epoch 1421/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.2005\n",
+            "Epoch 1422/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1995\n",
+            "Epoch 1423/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1986\n",
+            "Epoch 1424/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1977\n",
+            "Epoch 1425/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.1968\n",
+            "Epoch 1426/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1959\n",
+            "Epoch 1427/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1949\n",
+            "Epoch 1428/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1940\n",
+            "Epoch 1429/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1931\n",
+            "Epoch 1430/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1922\n",
+            "Epoch 1431/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1914\n",
+            "Epoch 1432/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1905\n",
+            "Epoch 1433/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1896\n",
+            "Epoch 1434/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1887\n",
+            "Epoch 1435/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1878\n",
+            "Epoch 1436/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1869\n",
+            "Epoch 1437/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1861\n",
+            "Epoch 1438/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1852\n",
+            "Epoch 1439/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1844\n",
+            "Epoch 1440/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1835\n",
+            "Epoch 1441/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1826\n",
+            "Epoch 1442/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1818\n",
+            "Epoch 1443/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1809\n",
+            "Epoch 1444/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1801\n",
+            "Epoch 1445/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1793\n",
+            "Epoch 1446/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1784\n",
+            "Epoch 1447/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1776\n",
+            "Epoch 1448/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1768\n",
+            "Epoch 1449/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1759\n",
+            "Epoch 1450/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1751\n",
+            "Epoch 1451/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1743\n",
+            "Epoch 1452/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1735\n",
+            "Epoch 1453/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1727\n",
+            "Epoch 1454/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1719\n",
+            "Epoch 1455/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1711\n",
+            "Epoch 1456/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1703\n",
+            "Epoch 1457/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1695\n",
+            "Epoch 1458/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1687\n",
+            "Epoch 1459/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1679\n",
+            "Epoch 1460/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1671\n",
+            "Epoch 1461/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.1663\n",
+            "Epoch 1462/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1656\n",
+            "Epoch 1463/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1648\n",
+            "Epoch 1464/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1640\n",
+            "Epoch 1465/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.1632\n",
+            "Epoch 1466/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1625\n",
+            "Epoch 1467/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.1617\n",
+            "Epoch 1468/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1610\n",
+            "Epoch 1469/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1602\n",
+            "Epoch 1470/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1595\n",
+            "Epoch 1471/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1587\n",
+            "Epoch 1472/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1580\n",
+            "Epoch 1473/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1572\n",
+            "Epoch 1474/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1565\n",
+            "Epoch 1475/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1558\n",
+            "Epoch 1476/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1550\n",
+            "Epoch 1477/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1543\n",
+            "Epoch 1478/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1536\n",
+            "Epoch 1479/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1529\n",
+            "Epoch 1480/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1521\n",
+            "Epoch 1481/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1514\n",
+            "Epoch 1482/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1507\n",
+            "Epoch 1483/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1500\n",
+            "Epoch 1484/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1493\n",
+            "Epoch 1485/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1486\n",
+            "Epoch 1486/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1479\n",
+            "Epoch 1487/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1472\n",
+            "Epoch 1488/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1465\n",
+            "Epoch 1489/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1458\n",
+            "Epoch 1490/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1451\n",
+            "Epoch 1491/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1445\n",
+            "Epoch 1492/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1438\n",
+            "Epoch 1493/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1431\n",
+            "Epoch 1494/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1424\n",
+            "Epoch 1495/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1418\n",
+            "Epoch 1496/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1411\n",
+            "Epoch 1497/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.1404\n",
+            "Epoch 1498/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.1398\n",
+            "Epoch 1499/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1391\n",
+            "Epoch 1500/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1385\n",
+            "Epoch 1501/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1378\n",
+            "Epoch 1502/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1372\n",
+            "Epoch 1503/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1365\n",
+            "Epoch 1504/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1359\n",
+            "Epoch 1505/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.1352\n",
+            "Epoch 1506/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1346\n",
+            "Epoch 1507/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.1340\n",
+            "Epoch 1508/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1333\n",
+            "Epoch 1509/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1327\n",
+            "Epoch 1510/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1321\n",
+            "Epoch 1511/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1315\n",
+            "Epoch 1512/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1308\n",
+            "Epoch 1513/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.1302\n",
+            "Epoch 1514/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1296\n",
+            "Epoch 1515/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1290\n",
+            "Epoch 1516/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1284\n",
+            "Epoch 1517/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.1278\n",
+            "Epoch 1518/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1272\n",
+            "Epoch 1519/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.1266\n",
+            "Epoch 1520/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1260\n",
+            "Epoch 1521/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1254\n",
+            "Epoch 1522/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1248\n",
+            "Epoch 1523/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1242\n",
+            "Epoch 1524/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1236\n",
+            "Epoch 1525/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1230\n",
+            "Epoch 1526/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1224\n",
+            "Epoch 1527/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1219\n",
+            "Epoch 1528/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1213\n",
+            "Epoch 1529/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1207\n",
+            "Epoch 1530/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1202\n",
+            "Epoch 1531/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.1196\n",
+            "Epoch 1532/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1190\n",
+            "Epoch 1533/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1185\n",
+            "Epoch 1534/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1179\n",
+            "Epoch 1535/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1173\n",
+            "Epoch 1536/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1168\n",
+            "Epoch 1537/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1162\n",
+            "Epoch 1538/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1157\n",
+            "Epoch 1539/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1151\n",
+            "Epoch 1540/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1146\n",
+            "Epoch 1541/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1140\n",
+            "Epoch 1542/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1135\n",
+            "Epoch 1543/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1130\n",
+            "Epoch 1544/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1124\n",
+            "Epoch 1545/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1119\n",
+            "Epoch 1546/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1114\n",
+            "Epoch 1547/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1109\n",
+            "Epoch 1548/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1103\n",
+            "Epoch 1549/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.1098\n",
+            "Epoch 1550/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1093\n",
+            "Epoch 1551/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1088\n",
+            "Epoch 1552/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1083\n",
+            "Epoch 1553/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1077\n",
+            "Epoch 1554/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1072\n",
+            "Epoch 1555/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1067\n",
+            "Epoch 1556/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1062\n",
+            "Epoch 1557/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1057\n",
+            "Epoch 1558/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1052\n",
+            "Epoch 1559/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1047\n",
+            "Epoch 1560/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.1042\n",
+            "Epoch 1561/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.1037\n",
+            "Epoch 1562/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.1032\n",
+            "Epoch 1563/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1028\n",
+            "Epoch 1564/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1023\n",
+            "Epoch 1565/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.1018\n",
+            "Epoch 1566/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1013\n",
+            "Epoch 1567/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1008\n",
+            "Epoch 1568/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1003\n",
+            "Epoch 1569/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0999\n",
+            "Epoch 1570/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0994\n",
+            "Epoch 1571/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0989\n",
+            "Epoch 1572/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0985\n",
+            "Epoch 1573/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0980\n",
+            "Epoch 1574/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0975\n",
+            "Epoch 1575/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0971\n",
+            "Epoch 1576/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0966\n",
+            "Epoch 1577/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0962\n",
+            "Epoch 1578/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0957\n",
+            "Epoch 1579/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0952\n",
+            "Epoch 1580/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0948\n",
+            "Epoch 1581/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0943\n",
+            "Epoch 1582/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0939\n",
+            "Epoch 1583/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0935\n",
+            "Epoch 1584/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0930\n",
+            "Epoch 1585/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0926\n",
+            "Epoch 1586/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0921\n",
+            "Epoch 1587/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0917\n",
+            "Epoch 1588/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0913\n",
+            "Epoch 1589/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0908\n",
+            "Epoch 1590/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0904\n",
+            "Epoch 1591/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0900\n",
+            "Epoch 1592/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0896\n",
+            "Epoch 1593/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0891\n",
+            "Epoch 1594/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0887\n",
+            "Epoch 1595/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0883\n",
+            "Epoch 1596/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0879\n",
+            "Epoch 1597/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0875\n",
+            "Epoch 1598/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0870\n",
+            "Epoch 1599/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0866\n",
+            "Epoch 1600/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0862\n",
+            "Epoch 1601/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0858\n",
+            "Epoch 1602/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0854\n",
+            "Epoch 1603/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0850\n",
+            "Epoch 1604/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0846\n",
+            "Epoch 1605/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0842\n",
+            "Epoch 1606/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0838\n",
+            "Epoch 1607/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0834\n",
+            "Epoch 1608/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0830\n",
+            "Epoch 1609/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0826\n",
+            "Epoch 1610/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0822\n",
+            "Epoch 1611/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0818\n",
+            "Epoch 1612/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0815\n",
+            "Epoch 1613/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0811\n",
+            "Epoch 1614/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0807\n",
+            "Epoch 1615/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0803\n",
+            "Epoch 1616/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0799\n",
+            "Epoch 1617/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0796\n",
+            "Epoch 1618/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0792\n",
+            "Epoch 1619/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0788\n",
+            "Epoch 1620/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0784\n",
+            "Epoch 1621/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0781\n",
+            "Epoch 1622/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0777\n",
+            "Epoch 1623/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0773\n",
+            "Epoch 1624/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0770\n",
+            "Epoch 1625/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0766\n",
+            "Epoch 1626/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0762\n",
+            "Epoch 1627/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0759\n",
+            "Epoch 1628/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0755\n",
+            "Epoch 1629/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0752\n",
+            "Epoch 1630/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0748\n",
+            "Epoch 1631/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0745\n",
+            "Epoch 1632/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0741\n",
+            "Epoch 1633/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0738\n",
+            "Epoch 1634/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0734\n",
+            "Epoch 1635/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0731\n",
+            "Epoch 1636/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0727\n",
+            "Epoch 1637/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0724\n",
+            "Epoch 1638/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0721\n",
+            "Epoch 1639/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0717\n",
+            "Epoch 1640/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0714\n",
+            "Epoch 1641/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0710\n",
+            "Epoch 1642/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0707\n",
+            "Epoch 1643/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0704\n",
+            "Epoch 1644/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0701\n",
+            "Epoch 1645/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0697\n",
+            "Epoch 1646/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0694\n",
+            "Epoch 1647/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0691\n",
+            "Epoch 1648/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0687\n",
+            "Epoch 1649/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0684\n",
+            "Epoch 1650/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0681\n",
+            "Epoch 1651/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0678\n",
+            "Epoch 1652/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0675\n",
+            "Epoch 1653/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0672\n",
+            "Epoch 1654/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0668\n",
+            "Epoch 1655/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0665\n",
+            "Epoch 1656/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0662\n",
+            "Epoch 1657/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0659\n",
+            "Epoch 1658/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0656\n",
+            "Epoch 1659/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0653\n",
+            "Epoch 1660/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0650\n",
+            "Epoch 1661/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0647\n",
+            "Epoch 1662/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0644\n",
+            "Epoch 1663/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0641\n",
+            "Epoch 1664/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0638\n",
+            "Epoch 1665/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0635\n",
+            "Epoch 1666/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0632\n",
+            "Epoch 1667/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0629\n",
+            "Epoch 1668/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0626\n",
+            "Epoch 1669/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0623\n",
+            "Epoch 1670/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0620\n",
+            "Epoch 1671/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0617\n",
+            "Epoch 1672/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0614\n",
+            "Epoch 1673/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0612\n",
+            "Epoch 1674/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0609\n",
+            "Epoch 1675/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0606\n",
+            "Epoch 1676/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0603\n",
+            "Epoch 1677/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0600\n",
+            "Epoch 1678/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0598\n",
+            "Epoch 1679/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0595\n",
+            "Epoch 1680/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0592\n",
+            "Epoch 1681/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0589\n",
+            "Epoch 1682/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0587\n",
+            "Epoch 1683/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0584\n",
+            "Epoch 1684/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0581\n",
+            "Epoch 1685/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0578\n",
+            "Epoch 1686/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0576\n",
+            "Epoch 1687/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0573\n",
+            "Epoch 1688/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0570\n",
+            "Epoch 1689/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0568\n",
+            "Epoch 1690/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0565\n",
+            "Epoch 1691/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0563\n",
+            "Epoch 1692/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0560\n",
+            "Epoch 1693/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0557\n",
+            "Epoch 1694/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0555\n",
+            "Epoch 1695/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0552\n",
+            "Epoch 1696/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0550\n",
+            "Epoch 1697/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0547\n",
+            "Epoch 1698/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0545\n",
+            "Epoch 1699/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0542\n",
+            "Epoch 1700/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0540\n",
+            "Epoch 1701/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0537\n",
+            "Epoch 1702/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0535\n",
+            "Epoch 1703/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0532\n",
+            "Epoch 1704/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0530\n",
+            "Epoch 1705/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0527\n",
+            "Epoch 1706/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0525\n",
+            "Epoch 1707/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0523\n",
+            "Epoch 1708/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0520\n",
+            "Epoch 1709/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0518\n",
+            "Epoch 1710/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0516\n",
+            "Epoch 1711/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0513\n",
+            "Epoch 1712/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0511\n",
+            "Epoch 1713/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0509\n",
+            "Epoch 1714/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0506\n",
+            "Epoch 1715/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0504\n",
+            "Epoch 1716/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0502\n",
+            "Epoch 1717/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0499\n",
+            "Epoch 1718/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0497\n",
+            "Epoch 1719/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0495\n",
+            "Epoch 1720/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0493\n",
+            "Epoch 1721/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0490\n",
+            "Epoch 1722/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0488\n",
+            "Epoch 1723/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0486\n",
+            "Epoch 1724/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0484\n",
+            "Epoch 1725/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0481\n",
+            "Epoch 1726/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0479\n",
+            "Epoch 1727/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0477\n",
+            "Epoch 1728/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0475\n",
+            "Epoch 1729/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0473\n",
+            "Epoch 1730/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0471\n",
+            "Epoch 1731/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0469\n",
+            "Epoch 1732/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0466\n",
+            "Epoch 1733/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0464\n",
+            "Epoch 1734/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0462\n",
+            "Epoch 1735/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0460\n",
+            "Epoch 1736/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0458\n",
+            "Epoch 1737/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0456\n",
+            "Epoch 1738/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0454\n",
+            "Epoch 1739/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0452\n",
+            "Epoch 1740/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0450\n",
+            "Epoch 1741/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0448\n",
+            "Epoch 1742/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0446\n",
+            "Epoch 1743/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0444\n",
+            "Epoch 1744/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0442\n",
+            "Epoch 1745/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0440\n",
+            "Epoch 1746/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0438\n",
+            "Epoch 1747/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0436\n",
+            "Epoch 1748/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0434\n",
+            "Epoch 1749/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0432\n",
+            "Epoch 1750/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0430\n",
+            "Epoch 1751/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0428\n",
+            "Epoch 1752/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0426\n",
+            "Epoch 1753/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0425\n",
+            "Epoch 1754/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0423\n",
+            "Epoch 1755/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0421\n",
+            "Epoch 1756/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0419\n",
+            "Epoch 1757/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0417\n",
+            "Epoch 1758/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0415\n",
+            "Epoch 1759/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0413\n",
+            "Epoch 1760/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0412\n",
+            "Epoch 1761/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0410\n",
+            "Epoch 1762/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0408\n",
+            "Epoch 1763/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0406\n",
+            "Epoch 1764/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0404\n",
+            "Epoch 1765/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0403\n",
+            "Epoch 1766/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0401\n",
+            "Epoch 1767/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0399\n",
+            "Epoch 1768/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0397\n",
+            "Epoch 1769/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0396\n",
+            "Epoch 1770/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0394\n",
+            "Epoch 1771/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0392\n",
+            "Epoch 1772/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0391\n",
+            "Epoch 1773/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0389\n",
+            "Epoch 1774/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0387\n",
+            "Epoch 1775/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0385\n",
+            "Epoch 1776/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0384\n",
+            "Epoch 1777/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0382\n",
+            "Epoch 1778/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0380\n",
+            "Epoch 1779/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0379\n",
+            "Epoch 1780/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0377\n",
+            "Epoch 1781/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0376\n",
+            "Epoch 1782/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0374\n",
+            "Epoch 1783/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0372\n",
+            "Epoch 1784/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0371\n",
+            "Epoch 1785/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0369\n",
+            "Epoch 1786/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0368\n",
+            "Epoch 1787/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0366\n",
+            "Epoch 1788/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0364\n",
+            "Epoch 1789/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0363\n",
+            "Epoch 1790/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0361\n",
+            "Epoch 1791/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0360\n",
+            "Epoch 1792/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0358\n",
+            "Epoch 1793/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0357\n",
+            "Epoch 1794/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0355\n",
+            "Epoch 1795/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0354\n",
+            "Epoch 1796/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0352\n",
+            "Epoch 1797/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0351\n",
+            "Epoch 1798/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0349\n",
+            "Epoch 1799/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0348\n",
+            "Epoch 1800/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0346\n",
+            "Epoch 1801/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0345\n",
+            "Epoch 1802/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0343\n",
+            "Epoch 1803/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0342\n",
+            "Epoch 1804/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0340\n",
+            "Epoch 1805/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0339\n",
+            "Epoch 1806/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0337\n",
+            "Epoch 1807/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0336\n",
+            "Epoch 1808/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0335\n",
+            "Epoch 1809/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0333\n",
+            "Epoch 1810/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0332\n",
+            "Epoch 1811/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0330\n",
+            "Epoch 1812/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0329\n",
+            "Epoch 1813/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0328\n",
+            "Epoch 1814/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0326\n",
+            "Epoch 1815/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0325\n",
+            "Epoch 1816/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0324\n",
+            "Epoch 1817/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0322\n",
+            "Epoch 1818/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0321\n",
+            "Epoch 1819/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0320\n",
+            "Epoch 1820/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0318\n",
+            "Epoch 1821/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0317\n",
+            "Epoch 1822/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0316\n",
+            "Epoch 1823/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0314\n",
+            "Epoch 1824/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0313\n",
+            "Epoch 1825/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0312\n",
+            "Epoch 1826/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0311\n",
+            "Epoch 1827/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0309\n",
+            "Epoch 1828/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0308\n",
+            "Epoch 1829/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0307\n",
+            "Epoch 1830/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0306\n",
+            "Epoch 1831/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0304\n",
+            "Epoch 1832/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0303\n",
+            "Epoch 1833/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0302\n",
+            "Epoch 1834/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0301\n",
+            "Epoch 1835/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0299\n",
+            "Epoch 1836/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0298\n",
+            "Epoch 1837/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0297\n",
+            "Epoch 1838/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0296\n",
+            "Epoch 1839/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0295\n",
+            "Epoch 1840/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0293\n",
+            "Epoch 1841/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0292\n",
+            "Epoch 1842/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0291\n",
+            "Epoch 1843/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0290\n",
+            "Epoch 1844/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0289\n",
+            "Epoch 1845/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0288\n",
+            "Epoch 1846/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0286\n",
+            "Epoch 1847/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0285\n",
+            "Epoch 1848/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0284\n",
+            "Epoch 1849/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0283\n",
+            "Epoch 1850/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0282\n",
+            "Epoch 1851/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0281\n",
+            "Epoch 1852/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0280\n",
+            "Epoch 1853/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0279\n",
+            "Epoch 1854/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0278\n",
+            "Epoch 1855/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0276\n",
+            "Epoch 1856/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0275\n",
+            "Epoch 1857/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0274\n",
+            "Epoch 1858/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0273\n",
+            "Epoch 1859/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0272\n",
+            "Epoch 1860/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0271\n",
+            "Epoch 1861/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0270\n",
+            "Epoch 1862/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0269\n",
+            "Epoch 1863/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0268\n",
+            "Epoch 1864/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0267\n",
+            "Epoch 1865/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0266\n",
+            "Epoch 1866/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0265\n",
+            "Epoch 1867/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0264\n",
+            "Epoch 1868/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0263\n",
+            "Epoch 1869/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0262\n",
+            "Epoch 1870/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0261\n",
+            "Epoch 1871/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0260\n",
+            "Epoch 1872/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0259\n",
+            "Epoch 1873/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0258\n",
+            "Epoch 1874/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0257\n",
+            "Epoch 1875/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0256\n",
+            "Epoch 1876/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0255\n",
+            "Epoch 1877/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0254\n",
+            "Epoch 1878/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0253\n",
+            "Epoch 1879/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0252\n",
+            "Epoch 1880/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0251\n",
+            "Epoch 1881/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0250\n",
+            "Epoch 1882/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0249\n",
+            "Epoch 1883/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0248\n",
+            "Epoch 1884/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0247\n",
+            "Epoch 1885/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0246\n",
+            "Epoch 1886/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0246\n",
+            "Epoch 1887/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0245\n",
+            "Epoch 1888/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0244\n",
+            "Epoch 1889/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0243\n",
+            "Epoch 1890/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0242\n",
+            "Epoch 1891/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0241\n",
+            "Epoch 1892/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0240\n",
+            "Epoch 1893/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0239\n",
+            "Epoch 1894/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0238\n",
+            "Epoch 1895/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0238\n",
+            "Epoch 1896/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0237\n",
+            "Epoch 1897/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0236\n",
+            "Epoch 1898/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0235\n",
+            "Epoch 1899/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0234\n",
+            "Epoch 1900/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0233\n",
+            "Epoch 1901/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0232\n",
+            "Epoch 1902/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0232\n",
+            "Epoch 1903/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0231\n",
+            "Epoch 1904/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0230\n",
+            "Epoch 1905/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0229\n",
+            "Epoch 1906/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0228\n",
+            "Epoch 1907/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0228\n",
+            "Epoch 1908/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0227\n",
+            "Epoch 1909/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0226\n",
+            "Epoch 1910/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0225\n",
+            "Epoch 1911/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0224\n",
+            "Epoch 1912/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0224\n",
+            "Epoch 1913/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0223\n",
+            "Epoch 1914/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0222\n",
+            "Epoch 1915/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0221\n",
+            "Epoch 1916/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0220\n",
+            "Epoch 1917/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0220\n",
+            "Epoch 1918/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0219\n",
+            "Epoch 1919/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0218\n",
+            "Epoch 1920/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0217\n",
+            "Epoch 1921/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0217\n",
+            "Epoch 1922/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0216\n",
+            "Epoch 1923/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0215\n",
+            "Epoch 1924/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0214\n",
+            "Epoch 1925/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0214\n",
+            "Epoch 1926/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0213\n",
+            "Epoch 1927/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0212\n",
+            "Epoch 1928/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0212\n",
+            "Epoch 1929/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0211\n",
+            "Epoch 1930/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0210\n",
+            "Epoch 1931/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0209\n",
+            "Epoch 1932/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0209\n",
+            "Epoch 1933/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0208\n",
+            "Epoch 1934/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0207\n",
+            "Epoch 1935/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0207\n",
+            "Epoch 1936/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0206\n",
+            "Epoch 1937/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0205\n",
+            "Epoch 1938/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0205\n",
+            "Epoch 1939/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0204\n",
+            "Epoch 1940/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0203\n",
+            "Epoch 1941/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0203\n",
+            "Epoch 1942/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0202\n",
+            "Epoch 1943/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0201\n",
+            "Epoch 1944/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0201\n",
+            "Epoch 1945/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0200\n",
+            "Epoch 1946/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0199\n",
+            "Epoch 1947/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0199\n",
+            "Epoch 1948/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0198\n",
+            "Epoch 1949/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0197\n",
+            "Epoch 1950/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0197\n",
+            "Epoch 1951/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0196\n",
+            "Epoch 1952/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0195\n",
+            "Epoch 1953/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0195\n",
+            "Epoch 1954/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0194\n",
+            "Epoch 1955/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0194\n",
+            "Epoch 1956/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0193\n",
+            "Epoch 1957/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0192\n",
+            "Epoch 1958/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0192\n",
+            "Epoch 1959/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0191\n",
+            "Epoch 1960/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0191\n",
+            "Epoch 1961/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0190\n",
+            "Epoch 1962/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0189\n",
+            "Epoch 1963/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0189\n",
+            "Epoch 1964/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0188\n",
+            "Epoch 1965/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0188\n",
+            "Epoch 1966/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0187\n",
+            "Epoch 1967/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0187\n",
+            "Epoch 1968/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0186\n",
+            "Epoch 1969/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0185\n",
+            "Epoch 1970/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0185\n",
+            "Epoch 1971/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0184\n",
+            "Epoch 1972/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0184\n",
+            "Epoch 1973/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0183\n",
+            "Epoch 1974/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0183\n",
+            "Epoch 1975/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0182\n",
+            "Epoch 1976/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0182\n",
+            "Epoch 1977/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0181\n",
+            "Epoch 1978/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0180\n",
+            "Epoch 1979/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0180\n",
+            "Epoch 1980/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0179\n",
+            "Epoch 1981/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0179\n",
+            "Epoch 1982/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0178\n",
+            "Epoch 1983/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0178\n",
+            "Epoch 1984/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0177\n",
+            "Epoch 1985/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0177\n",
+            "Epoch 1986/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0176\n",
+            "Epoch 1987/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0176\n",
+            "Epoch 1988/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0175\n",
+            "Epoch 1989/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0175\n",
+            "Epoch 1990/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0174\n",
+            "Epoch 1991/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0174\n",
+            "Epoch 1992/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0173\n",
+            "Epoch 1993/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0173\n",
+            "Epoch 1994/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0172\n",
+            "Epoch 1995/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0172\n",
+            "Epoch 1996/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0171\n",
+            "Epoch 1997/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0171\n",
+            "Epoch 1998/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0170\n",
+            "Epoch 1999/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0170\n",
+            "Epoch 2000/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0170\n",
+            "Epoch 2000/3000\n",
+            " - loss: 0.0170\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0170\n",
+            "Epoch 2001/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0169\n",
+            "Epoch 2002/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0169\n",
+            "Epoch 2003/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0168\n",
+            "Epoch 2004/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0168\n",
+            "Epoch 2005/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0167\n",
+            "Epoch 2006/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0167\n",
+            "Epoch 2007/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0166\n",
+            "Epoch 2008/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0166\n",
+            "Epoch 2009/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0165\n",
+            "Epoch 2010/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0165\n",
+            "Epoch 2011/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0165\n",
+            "Epoch 2012/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0164\n",
+            "Epoch 2013/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0164\n",
+            "Epoch 2014/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0163\n",
+            "Epoch 2015/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0163\n",
+            "Epoch 2016/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0162\n",
+            "Epoch 2017/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0162\n",
+            "Epoch 2018/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0162\n",
+            "Epoch 2019/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0161\n",
+            "Epoch 2020/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0161\n",
+            "Epoch 2021/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0160\n",
+            "Epoch 2022/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0160\n",
+            "Epoch 2023/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0159\n",
+            "Epoch 2024/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0159\n",
+            "Epoch 2025/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0159\n",
+            "Epoch 2026/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0158\n",
+            "Epoch 2027/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0158\n",
+            "Epoch 2028/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0157\n",
+            "Epoch 2029/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0157\n",
+            "Epoch 2030/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0157\n",
+            "Epoch 2031/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0156\n",
+            "Epoch 2032/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0156\n",
+            "Epoch 2033/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0156\n",
+            "Epoch 2034/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0155\n",
+            "Epoch 2035/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0155\n",
+            "Epoch 2036/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0154\n",
+            "Epoch 2037/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0154\n",
+            "Epoch 2038/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0154\n",
+            "Epoch 2039/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0153\n",
+            "Epoch 2040/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0153\n",
+            "Epoch 2041/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0153\n",
+            "Epoch 2042/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0152\n",
+            "Epoch 2043/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0152\n",
+            "Epoch 2044/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0151\n",
+            "Epoch 2045/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0151\n",
+            "Epoch 2046/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0151\n",
+            "Epoch 2047/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0150\n",
+            "Epoch 2048/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0150\n",
+            "Epoch 2049/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0150\n",
+            "Epoch 2050/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0149\n",
+            "Epoch 2051/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0149\n",
+            "Epoch 2052/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0149\n",
+            "Epoch 2053/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0148\n",
+            "Epoch 2054/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0148\n",
+            "Epoch 2055/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0148\n",
+            "Epoch 2056/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0147\n",
+            "Epoch 2057/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0147\n",
+            "Epoch 2058/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0147\n",
+            "Epoch 2059/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0146\n",
+            "Epoch 2060/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0146\n",
+            "Epoch 2061/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0146\n",
+            "Epoch 2062/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0145\n",
+            "Epoch 2063/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0145\n",
+            "Epoch 2064/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0145\n",
+            "Epoch 2065/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0144\n",
+            "Epoch 2066/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0144\n",
+            "Epoch 2067/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0144\n",
+            "Epoch 2068/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0144\n",
+            "Epoch 2069/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0143\n",
+            "Epoch 2070/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0143\n",
+            "Epoch 2071/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0143\n",
+            "Epoch 2072/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0142\n",
+            "Epoch 2073/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0142\n",
+            "Epoch 2074/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0142\n",
+            "Epoch 2075/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0141\n",
+            "Epoch 2076/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0141\n",
+            "Epoch 2077/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0141\n",
+            "Epoch 2078/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0141\n",
+            "Epoch 2079/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0140\n",
+            "Epoch 2080/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0140\n",
+            "Epoch 2081/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0140\n",
+            "Epoch 2082/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0139\n",
+            "Epoch 2083/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0139\n",
+            "Epoch 2084/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0139\n",
+            "Epoch 2085/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0139\n",
+            "Epoch 2086/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0138\n",
+            "Epoch 2087/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0138\n",
+            "Epoch 2088/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0138\n",
+            "Epoch 2089/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0137\n",
+            "Epoch 2090/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0137\n",
+            "Epoch 2091/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0137\n",
+            "Epoch 2092/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0137\n",
+            "Epoch 2093/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0136\n",
+            "Epoch 2094/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0136\n",
+            "Epoch 2095/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0136\n",
+            "Epoch 2096/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0136\n",
+            "Epoch 2097/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0135\n",
+            "Epoch 2098/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0135\n",
+            "Epoch 2099/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0135\n",
+            "Epoch 2100/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0135\n",
+            "Epoch 2101/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0134\n",
+            "Epoch 2102/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0134\n",
+            "Epoch 2103/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0134\n",
+            "Epoch 2104/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0134\n",
+            "Epoch 2105/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0133\n",
+            "Epoch 2106/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0133\n",
+            "Epoch 2107/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0133\n",
+            "Epoch 2108/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0133\n",
+            "Epoch 2109/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0132\n",
+            "Epoch 2110/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0132\n",
+            "Epoch 2111/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0132\n",
+            "Epoch 2112/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0132\n",
+            "Epoch 2113/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0131\n",
+            "Epoch 2114/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0131\n",
+            "Epoch 2115/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0131\n",
+            "Epoch 2116/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0131\n",
+            "Epoch 2117/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0131\n",
+            "Epoch 2118/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0130\n",
+            "Epoch 2119/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0130\n",
+            "Epoch 2120/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0130\n",
+            "Epoch 2121/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0130\n",
+            "Epoch 2122/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0129\n",
+            "Epoch 2123/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0129\n",
+            "Epoch 2124/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0129\n",
+            "Epoch 2125/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0129\n",
+            "Epoch 2126/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0129\n",
+            "Epoch 2127/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0128\n",
+            "Epoch 2128/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0128\n",
+            "Epoch 2129/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0128\n",
+            "Epoch 2130/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0128\n",
+            "Epoch 2131/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0128\n",
+            "Epoch 2132/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0127\n",
+            "Epoch 2133/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0127\n",
+            "Epoch 2134/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0127\n",
+            "Epoch 2135/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0127\n",
+            "Epoch 2136/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0127\n",
+            "Epoch 2137/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0126\n",
+            "Epoch 2138/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0126\n",
+            "Epoch 2139/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0126\n",
+            "Epoch 2140/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0126\n",
+            "Epoch 2141/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0126\n",
+            "Epoch 2142/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0125\n",
+            "Epoch 2143/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0125\n",
+            "Epoch 2144/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0125\n",
+            "Epoch 2145/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0125\n",
+            "Epoch 2146/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0125\n",
+            "Epoch 2147/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0124\n",
+            "Epoch 2148/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0124\n",
+            "Epoch 2149/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0124\n",
+            "Epoch 2150/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0124\n",
+            "Epoch 2151/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0124\n",
+            "Epoch 2152/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0124\n",
+            "Epoch 2153/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0123\n",
+            "Epoch 2154/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0123\n",
+            "Epoch 2155/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0123\n",
+            "Epoch 2156/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0123\n",
+            "Epoch 2157/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0123\n",
+            "Epoch 2158/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0122\n",
+            "Epoch 2159/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0122\n",
+            "Epoch 2160/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0122\n",
+            "Epoch 2161/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0122\n",
+            "Epoch 2162/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0122\n",
+            "Epoch 2163/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0122\n",
+            "Epoch 2164/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0121\n",
+            "Epoch 2165/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0121\n",
+            "Epoch 2166/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0121\n",
+            "Epoch 2167/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0121\n",
+            "Epoch 2168/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0121\n",
+            "Epoch 2169/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0121\n",
+            "Epoch 2170/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0120\n",
+            "Epoch 2171/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0120\n",
+            "Epoch 2172/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0120\n",
+            "Epoch 2173/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0120\n",
+            "Epoch 2174/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0120\n",
+            "Epoch 2175/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0120\n",
+            "Epoch 2176/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0120\n",
+            "Epoch 2177/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0119\n",
+            "Epoch 2178/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0119\n",
+            "Epoch 2179/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0119\n",
+            "Epoch 2180/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0119\n",
+            "Epoch 2181/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0119\n",
+            "Epoch 2182/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0119\n",
+            "Epoch 2183/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0119\n",
+            "Epoch 2184/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0118\n",
+            "Epoch 2185/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0118\n",
+            "Epoch 2186/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0118\n",
+            "Epoch 2187/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0118\n",
+            "Epoch 2188/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0118\n",
+            "Epoch 2189/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0118\n",
+            "Epoch 2190/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0118\n",
+            "Epoch 2191/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0117\n",
+            "Epoch 2192/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0117\n",
+            "Epoch 2193/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0117\n",
+            "Epoch 2194/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0117\n",
+            "Epoch 2195/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0117\n",
+            "Epoch 2196/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0117\n",
+            "Epoch 2197/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0117\n",
+            "Epoch 2198/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0116\n",
+            "Epoch 2199/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0116\n",
+            "Epoch 2200/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0116\n",
+            "Epoch 2201/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0116\n",
+            "Epoch 2202/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0116\n",
+            "Epoch 2203/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0116\n",
+            "Epoch 2204/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0116\n",
+            "Epoch 2205/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0116\n",
+            "Epoch 2206/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0115\n",
+            "Epoch 2207/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0115\n",
+            "Epoch 2208/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0115\n",
+            "Epoch 2209/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0115\n",
+            "Epoch 2210/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0115\n",
+            "Epoch 2211/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0115\n",
+            "Epoch 2212/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0115\n",
+            "Epoch 2213/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0115\n",
+            "Epoch 2214/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0114\n",
+            "Epoch 2215/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0114\n",
+            "Epoch 2216/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0114\n",
+            "Epoch 2217/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0114\n",
+            "Epoch 2218/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0114\n",
+            "Epoch 2219/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0114\n",
+            "Epoch 2220/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0114\n",
+            "Epoch 2221/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0114\n",
+            "Epoch 2222/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0114\n",
+            "Epoch 2223/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0113\n",
+            "Epoch 2224/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0113\n",
+            "Epoch 2225/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0113\n",
+            "Epoch 2226/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0113\n",
+            "Epoch 2227/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0113\n",
+            "Epoch 2228/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0113\n",
+            "Epoch 2229/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0113\n",
+            "Epoch 2230/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0113\n",
+            "Epoch 2231/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0113\n",
+            "Epoch 2232/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0112\n",
+            "Epoch 2233/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0112\n",
+            "Epoch 2234/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0112\n",
+            "Epoch 2235/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0112\n",
+            "Epoch 2236/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0112\n",
+            "Epoch 2237/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0112\n",
+            "Epoch 2238/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0112\n",
+            "Epoch 2239/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0112\n",
+            "Epoch 2240/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0112\n",
+            "Epoch 2241/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0112\n",
+            "Epoch 2242/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0111\n",
+            "Epoch 2243/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0111\n",
+            "Epoch 2244/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0111\n",
+            "Epoch 2245/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0111\n",
+            "Epoch 2246/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0111\n",
+            "Epoch 2247/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0111\n",
+            "Epoch 2248/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0111\n",
+            "Epoch 2249/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0111\n",
+            "Epoch 2250/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0111\n",
+            "Epoch 2251/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0111\n",
+            "Epoch 2252/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0110\n",
+            "Epoch 2253/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0110\n",
+            "Epoch 2254/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0110\n",
+            "Epoch 2255/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0110\n",
+            "Epoch 2256/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0110\n",
+            "Epoch 2257/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0110\n",
+            "Epoch 2258/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0110\n",
+            "Epoch 2259/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0110\n",
+            "Epoch 2260/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0110\n",
+            "Epoch 2261/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0110\n",
+            "Epoch 2262/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0110\n",
+            "Epoch 2263/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0110\n",
+            "Epoch 2264/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0109\n",
+            "Epoch 2265/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0109\n",
+            "Epoch 2266/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0109\n",
+            "Epoch 2267/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0109\n",
+            "Epoch 2268/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0109\n",
+            "Epoch 2269/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0109\n",
+            "Epoch 2270/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0109\n",
+            "Epoch 2271/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0109\n",
+            "Epoch 2272/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0109\n",
+            "Epoch 2273/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0109\n",
+            "Epoch 2274/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0109\n",
+            "Epoch 2275/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0109\n",
+            "Epoch 2276/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0108\n",
+            "Epoch 2277/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0108\n",
+            "Epoch 2278/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0108\n",
+            "Epoch 2279/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0108\n",
+            "Epoch 2280/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0108\n",
+            "Epoch 2281/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0108\n",
+            "Epoch 2282/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0108\n",
+            "Epoch 2283/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0108\n",
+            "Epoch 2284/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0108\n",
+            "Epoch 2285/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0108\n",
+            "Epoch 2286/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0108\n",
+            "Epoch 2287/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0108\n",
+            "Epoch 2288/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0108\n",
+            "Epoch 2289/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0108\n",
+            "Epoch 2290/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0107\n",
+            "Epoch 2291/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0107\n",
+            "Epoch 2292/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0107\n",
+            "Epoch 2293/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0107\n",
+            "Epoch 2294/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0107\n",
+            "Epoch 2295/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0107\n",
+            "Epoch 2296/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0107\n",
+            "Epoch 2297/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0107\n",
+            "Epoch 2298/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0107\n",
+            "Epoch 2299/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0107\n",
+            "Epoch 2300/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0107\n",
+            "Epoch 2301/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0107\n",
+            "Epoch 2302/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0107\n",
+            "Epoch 2303/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0107\n",
+            "Epoch 2304/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0107\n",
+            "Epoch 2305/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0106\n",
+            "Epoch 2306/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0106\n",
+            "Epoch 2307/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0106\n",
+            "Epoch 2308/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0106\n",
+            "Epoch 2309/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0106\n",
+            "Epoch 2310/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0106\n",
+            "Epoch 2311/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0106\n",
+            "Epoch 2312/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0106\n",
+            "Epoch 2313/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0106\n",
+            "Epoch 2314/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n",
+            "Epoch 2315/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0106\n",
+            "Epoch 2316/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0106\n",
+            "Epoch 2317/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0106\n",
+            "Epoch 2318/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0106\n",
+            "Epoch 2319/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0106\n",
+            "Epoch 2320/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n",
+            "Epoch 2321/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0106\n",
+            "Epoch 2322/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0105\n",
+            "Epoch 2323/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0105\n",
+            "Epoch 2324/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0105\n",
+            "Epoch 2325/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0105\n",
+            "Epoch 2326/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0105\n",
+            "Epoch 2327/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0105\n",
+            "Epoch 2328/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0105\n",
+            "Epoch 2329/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0105\n",
+            "Epoch 2330/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0105\n",
+            "Epoch 2331/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0105\n",
+            "Epoch 2332/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0105\n",
+            "Epoch 2333/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0105\n",
+            "Epoch 2334/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0105\n",
+            "Epoch 2335/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0105\n",
+            "Epoch 2336/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n",
+            "Epoch 2337/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0105\n",
+            "Epoch 2338/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0105\n",
+            "Epoch 2339/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0105\n",
+            "Epoch 2340/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0105\n",
+            "Epoch 2341/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0104\n",
+            "Epoch 2342/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0104\n",
+            "Epoch 2343/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0104\n",
+            "Epoch 2344/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0104\n",
+            "Epoch 2345/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0104\n",
+            "Epoch 2346/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0104\n",
+            "Epoch 2347/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0104\n",
+            "Epoch 2348/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0104\n",
+            "Epoch 2349/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0104\n",
+            "Epoch 2350/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0104\n",
+            "Epoch 2351/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0104\n",
+            "Epoch 2352/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0104\n",
+            "Epoch 2353/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0104\n",
+            "Epoch 2354/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0104\n",
+            "Epoch 2355/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0104\n",
+            "Epoch 2356/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0104\n",
+            "Epoch 2357/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0104\n",
+            "Epoch 2358/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0104\n",
+            "Epoch 2359/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0104\n",
+            "Epoch 2360/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0104\n",
+            "Epoch 2361/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0104\n",
+            "Epoch 2362/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0104\n",
+            "Epoch 2363/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0103\n",
+            "Epoch 2364/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0103\n",
+            "Epoch 2365/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0103\n",
+            "Epoch 2366/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0103\n",
+            "Epoch 2367/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n",
+            "Epoch 2368/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0103\n",
+            "Epoch 2369/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0103\n",
+            "Epoch 2370/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0103\n",
+            "Epoch 2371/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n",
+            "Epoch 2372/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n",
+            "Epoch 2373/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n",
+            "Epoch 2374/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0103\n",
+            "Epoch 2375/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0103\n",
+            "Epoch 2376/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0103\n",
+            "Epoch 2377/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0103\n",
+            "Epoch 2378/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0103\n",
+            "Epoch 2379/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0103\n",
+            "Epoch 2380/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0103\n",
+            "Epoch 2381/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0103\n",
+            "Epoch 2382/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0103\n",
+            "Epoch 2383/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0103\n",
+            "Epoch 2384/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0103\n",
+            "Epoch 2385/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0103\n",
+            "Epoch 2386/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0103\n",
+            "Epoch 2387/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0103\n",
+            "Epoch 2388/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0103\n",
+            "Epoch 2389/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0103\n",
+            "Epoch 2390/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0102\n",
+            "Epoch 2391/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0102\n",
+            "Epoch 2392/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0102\n",
+            "Epoch 2393/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0102\n",
+            "Epoch 2394/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0102\n",
+            "Epoch 2395/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0102\n",
+            "Epoch 2396/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0102\n",
+            "Epoch 2397/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0102\n",
+            "Epoch 2398/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0102\n",
+            "Epoch 2399/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0102\n",
+            "Epoch 2400/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0102\n",
+            "Epoch 2401/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0102\n",
+            "Epoch 2402/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0102\n",
+            "Epoch 2403/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0102\n",
+            "Epoch 2404/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0102\n",
+            "Epoch 2405/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0102\n",
+            "Epoch 2406/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0102\n",
+            "Epoch 2407/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0102\n",
+            "Epoch 2408/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0102\n",
+            "Epoch 2409/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0102\n",
+            "Epoch 2410/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0102\n",
+            "Epoch 2411/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0102\n",
+            "Epoch 2412/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0102\n",
+            "Epoch 2413/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0102\n",
+            "Epoch 2414/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0102\n",
+            "Epoch 2415/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0102\n",
+            "Epoch 2416/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0102\n",
+            "Epoch 2417/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0102\n",
+            "Epoch 2418/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0102\n",
+            "Epoch 2419/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0102\n",
+            "Epoch 2420/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0102\n",
+            "Epoch 2421/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0102\n",
+            "Epoch 2422/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0102\n",
+            "Epoch 2423/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0102\n",
+            "Epoch 2424/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0101\n",
+            "Epoch 2425/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0101\n",
+            "Epoch 2426/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n",
+            "Epoch 2427/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0101\n",
+            "Epoch 2428/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0101\n",
+            "Epoch 2429/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0101\n",
+            "Epoch 2430/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0101\n",
+            "Epoch 2431/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0101\n",
+            "Epoch 2432/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0101\n",
+            "Epoch 2433/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0101\n",
+            "Epoch 2434/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0101\n",
+            "Epoch 2435/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0101\n",
+            "Epoch 2436/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0101\n",
+            "Epoch 2437/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0101\n",
+            "Epoch 2438/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0101\n",
+            "Epoch 2439/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0101\n",
+            "Epoch 2440/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n",
+            "Epoch 2441/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0101\n",
+            "Epoch 2442/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0101\n",
+            "Epoch 2443/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0101\n",
+            "Epoch 2444/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n",
+            "Epoch 2445/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0101\n",
+            "Epoch 2446/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0101\n",
+            "Epoch 2447/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0101\n",
+            "Epoch 2448/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0101\n",
+            "Epoch 2449/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0101\n",
+            "Epoch 2450/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0101\n",
+            "Epoch 2451/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0101\n",
+            "Epoch 2452/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0101\n",
+            "Epoch 2453/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0101\n",
+            "Epoch 2454/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0101\n",
+            "Epoch 2455/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0101\n",
+            "Epoch 2456/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0101\n",
+            "Epoch 2457/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0101\n",
+            "Epoch 2458/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0101\n",
+            "Epoch 2459/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0101\n",
+            "Epoch 2460/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0101\n",
+            "Epoch 2461/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0101\n",
+            "Epoch 2462/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0101\n",
+            "Epoch 2463/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0101\n",
+            "Epoch 2464/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0101\n",
+            "Epoch 2465/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0101\n",
+            "Epoch 2466/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0101\n",
+            "Epoch 2467/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0101\n",
+            "Epoch 2468/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n",
+            "Epoch 2469/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2470/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0100\n",
+            "Epoch 2471/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2472/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n",
+            "Epoch 2473/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2474/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0100\n",
+            "Epoch 2475/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n",
+            "Epoch 2476/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0100\n",
+            "Epoch 2477/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2478/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2479/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n",
+            "Epoch 2480/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0100\n",
+            "Epoch 2481/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2482/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0100\n",
+            "Epoch 2483/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0100\n",
+            "Epoch 2484/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2485/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0100\n",
+            "Epoch 2486/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2487/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n",
+            "Epoch 2488/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0100\n",
+            "Epoch 2489/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2490/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0100\n",
+            "Epoch 2491/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0100\n",
+            "Epoch 2492/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0100\n",
+            "Epoch 2493/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2494/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2495/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n",
+            "Epoch 2496/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0100\n",
+            "Epoch 2497/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2498/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0100\n",
+            "Epoch 2499/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0100\n",
+            "Epoch 2500/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0100\n",
+            "Epoch 2501/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0100\n",
+            "Epoch 2502/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2503/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0100\n",
+            "Epoch 2504/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0100\n",
+            "Epoch 2505/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2506/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n",
+            "Epoch 2507/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2508/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0100\n",
+            "Epoch 2509/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0100\n",
+            "Epoch 2510/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2511/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2512/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2513/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2514/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2515/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0100\n",
+            "Epoch 2516/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2517/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2518/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2519/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n",
+            "Epoch 2520/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2521/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0100\n",
+            "Epoch 2522/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2523/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2524/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0100\n",
+            "Epoch 2525/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2526/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n",
+            "Epoch 2527/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2528/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n",
+            "Epoch 2529/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0100\n",
+            "Epoch 2530/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0100\n",
+            "Epoch 2531/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0100\n",
+            "Epoch 2532/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0100\n",
+            "Epoch 2533/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n",
+            "Epoch 2534/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n",
+            "Epoch 2535/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2536/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0100\n",
+            "Epoch 2537/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0100\n",
+            "Epoch 2538/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0100\n",
+            "Epoch 2539/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2540/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n",
+            "Epoch 2541/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0099\n",
+            "Epoch 2542/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0099\n",
+            "Epoch 2543/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0099\n",
+            "Epoch 2544/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0099\n",
+            "Epoch 2545/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0099\n",
+            "Epoch 2546/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0099\n",
+            "Epoch 2547/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n",
+            "Epoch 2548/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0099\n",
+            "Epoch 2549/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0099\n",
+            "Epoch 2550/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0099\n",
+            "Epoch 2551/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0099\n",
+            "Epoch 2552/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n",
+            "Epoch 2553/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0099\n",
+            "Epoch 2554/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0099\n",
+            "Epoch 2555/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0099\n",
+            "Epoch 2556/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0099\n",
+            "Epoch 2557/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0099\n",
+            "Epoch 2558/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0099\n",
+            "Epoch 2559/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0099\n",
+            "Epoch 2560/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0099\n",
+            "Epoch 2561/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0099\n",
+            "Epoch 2562/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0099\n",
+            "Epoch 2563/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0099\n",
+            "Epoch 2564/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0099\n",
+            "Epoch 2565/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0099\n",
+            "Epoch 2566/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0099\n",
+            "Epoch 2567/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0099\n",
+            "Epoch 2568/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0099\n",
+            "Epoch 2569/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0099\n",
+            "Epoch 2570/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0099\n",
+            "Epoch 2571/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n",
+            "Epoch 2572/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n",
+            "Epoch 2573/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0099\n",
+            "Epoch 2574/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0099\n",
+            "Epoch 2575/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n",
+            "Epoch 2576/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0099\n",
+            "Epoch 2577/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0099\n",
+            "Epoch 2578/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0099\n",
+            "Epoch 2579/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0099\n",
+            "Epoch 2580/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0099\n",
+            "Epoch 2581/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0099\n",
+            "Epoch 2582/3000\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0099\n",
+            "Epoch 2582: early stopping\n",
+            "Restoring model weights from the end of the best epoch: 2282.\n",
+            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# обучение AE2\n",
+        "ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt',\n",
+        "3000, True, patience)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "03194c1b-c03e-42a5-93e5-f4d007ddb7c7",
+      "metadata": {
+        "id": "03194c1b-c03e-42a5-93e5-f4d007ddb7c7"
+      },
+      "source": [
+        "### 6) Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали второй порог ошибки реконструкции – порог обнаружения аномалий."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ошибка MSE_AE2 = 0.0108"
+      ],
+      "metadata": {
+        "id": "6TBPoMskhCQp"
+      },
+      "id": "6TBPoMskhCQp"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "47cf147a-f97b-41d3-9d7c-b799da0115fe",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 718
+        },
+        "id": "47cf147a-f97b-41d3-9d7c-b799da0115fe",
+        "outputId": "fb9fcf28-01a2-4c3b-adfb-21e50ba09ea1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE2, IREth2, 'AE2')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fd14d331-aad6-4b98-9926-5ab6a055beb0",
+      "metadata": {
+        "id": "fd14d331-aad6-4b98-9926-5ab6a055beb0"
+      },
+      "source": [
+        "### 7) Рассчитали характеристики качества обучения EDCA для AE1 и AE2. Визуализировали и сравнили области пространства признаков, распознаваемые автокодировщиками AE1 и AE2. Сделали вывод о пригодности AE1 и AE2 для качественного обнаружения аномалий.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "58fd0b46-1064-4f2f-b04f-88307bf938d2",
+      "metadata": {
+        "id": "58fd0b46-1064-4f2f-b04f-88307bf938d2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "959a4367-d0cf-4e34-b46e-52b1154438e0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "amount:  21\n",
+            "amount_ae:  287\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x700 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2200x800 with 3 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Оценка качества AE1\n",
+            "IDEAL = 0. Excess:  12.666666666666666\n",
+            "IDEAL = 0. Deficit:  0.0\n",
+            "IDEAL = 1. Coating:  1.0\n",
+            "summa:  1.0\n",
+            "IDEAL = 1. Extrapolation precision (Approx):  0.07317073170731707\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# построение областей покрытия и границ классов\n",
+        "# расчет характеристик качества обучения\n",
+        "numb_square = 20\n",
+        "xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# построение областей покрытия и границ классов\n",
+        "# расчет характеристик качества обучения\n",
+        "numb_square = 20\n",
+        "xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ZG72thVfYMOy",
+        "outputId": "7e29f0bc-47f2-4b87-bed3-6f762eaad94a"
+      },
+      "id": "ZG72thVfYMOy",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "amount:  21\n",
+            "amount_ae:  28\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x700 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2200x800 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Оценка качества AE2\n",
+            "IDEAL = 0. Excess:  0.3333333333333333\n",
+            "IDEAL = 0. Deficit:  0.0\n",
+            "IDEAL = 1. Coating:  1.0\n",
+            "summa:  1.0\n",
+            "IDEAL = 1. Extrapolation precision (Approx):  0.75\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сравнение характеристик качества обучения и областей аппроксимации\n",
+        "lib.plot2in1(data, xx, yy, Z1, Z2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "RJWYYYHQbUIN",
+        "outputId": "4690b6e4-1289-478b-8406-d23196cb5fbd"
+      },
+      "id": "RJWYYYHQbUIN",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d766b8f0-3ba7-4a73-94a0-7858eb2f5515",
+      "metadata": {
+        "id": "d766b8f0-3ba7-4a73-94a0-7858eb2f5515"
+      },
+      "source": [
+        "### 8) Если автокодировщик AE2 недостаточно точно аппроксимирует область обучающих данных, то подобрать подходящие параметры автокодировщика и повторить шаги (6) – (8)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Полученные показатели EDCA для автокодировщика AE2 нас устраивают."
+      ],
+      "metadata": {
+        "id": "XbnbvTima5dv"
+      },
+      "id": "XbnbvTima5dv"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cfa38b82-4c8e-4437-9c25-581d7fea0f2c",
+      "metadata": {
+        "id": "cfa38b82-4c8e-4437-9c25-581d7fea0f2c"
+      },
+      "source": [
+        "### 9) Изучили сохраненный набор данных и пространство признаков. Создали тестовую выборку, состоящую, как минимум, из 4ёх элементов, не входящих в обучающую выборку. Элементы должны быть такими, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d618382f-f65a-4c17-b0d0-3d2d0c4bc50e",
+      "metadata": {
+        "id": "d618382f-f65a-4c17-b0d0-3d2d0c4bc50e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "14d12cfb-d8c4-411c-9f70-f18a92250d9f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[4.52855237 4.55922899]\n",
+            " [4.64838873 4.69473527]\n",
+            " [4.41414674 4.47819019]\n",
+            " [4.43922161 4.43454531]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# загрузка тестового набора\n",
+        "data_test = np.loadtxt('data_test.txt', dtype=float)\n",
+        "print(data_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 10) Применили обученные автокодировщики AE1 и AE2 к тестовым данным и вывели значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога на график и в консоль."
+      ],
+      "metadata": {
+        "id": "3Ce8wSVMdjBj"
+      },
+      "id": "3Ce8wSVMdjBj"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE1\n",
+        "predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Hq8A0LyMcKXk",
+        "outputId": "333360fa-342a-48ab-c226-027220719947"
+      },
+      "id": "Hq8A0LyMcKXk",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE1\n",
+        "lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)\n",
+        "lib.ire_plot('test', ire1, IREth1, 'AE1')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 744
+        },
+        "id": "Z_v2X4HvcQD5",
+        "outputId": "801796f4-d92d-4a5f-d140-e5cdf4f04ae2"
+      },
+      "id": "Z_v2X4HvcQD5",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Аномалий не обнаружено\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fT75bpEnc7a2",
+        "outputId": "6523b469-d5df-4cb8-f3bc-9a47de13a7b6"
+      },
+      "id": "fT75bpEnc7a2",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)\n",
+        "lib.ire_plot('test', ire2, IREth2, 'AE2')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 846
+        },
+        "id": "EpGtgYZVdAen",
+        "outputId": "b0f17d15-a252-47b8-b721-08292e1a92a9"
+      },
+      "id": "EpGtgYZVdAen",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "i         Labels    IRE       IREth     \n",
+            "0         [1.]      [2.02]    0.4       \n",
+            "1         [1.]      [1.84]    0.4       \n",
+            "2         [1.]      [2.16]    0.4       \n",
+            "3         [1.]      [2.17]    0.4       \n",
+            "Обнаружено  4.0  аномалий\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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nnXdmHVo2tDVr1sTxxx8fb7/9dkREbL/99vHEE09Er169GrkyAGge3B4OADSK8847L2666abM9qc//SlefvnleP3112PfffeNiNKAbtSoUfHBBx80crX1p2wBnoioEMjV5PPtVq9eXWP7du3a1b2werZy5cqt7mPTpk0V3g8YMCBef/31+O///u8KzwHdtGlTzJw5M37961/HiBEjok+fPnHbbbdt9edvifqocWt/dmW3KSfVwoUL48knn8y8/8Y3vlFj+/K3jr/88ssxZ86cBqstWxs2bIgvf/nLmUdZdOrUKR577LEYMGBAI1cGAM2H0BIASJSBAwfGk08+mZmFuWbNmjjzzDMbuar607Fjx8zrtWvXZnXO59t16tSpXmtqCOWD1l//+teRTqfrvJWfYVqmoKAgbrjhhli0aFFMmzYtrrzyyjjuuOMqzDz95JNP4qyzzopzzz13W1xqvddY/mf34IMPbtHPLsnuueeeCsHqEUccUeH5tp/f9t577wrnl5+l2RiKi4vjxBNPzDxftl27dvG3v/0tDjrooEatCwCaG6ElAJA43bt3jxtvvDHzftq0afHoo482YkX1p/yt2/Pnz8/qnLlz52Zet2nTpkmElgUFBZnXCxcurPf+8/Ly4ogjjoiLL744Jk+eHEuXLo3HHnssvvCFL2Ta3HjjjXVaPTopNTb0z66xbW3oeM8990RJSUk9VVM3mzdvjtNOOy2zWFJubm7cf//9ccQRRzRKPQDQnAktAYBEKlu8pMzFF1/ciNXUn/322y/z+uWXX87qVt4XXnihwvnZrCZd3+r6mYccckjm9T/+8Y/6LqeS3NzcOPbYY+Opp56qMDPvb3/7W6W2jfHzi8i+xob42TXWNX/eP//5z3jjjTcy7w866KA45JBDstrKFq36+OOPY+rUqdu89pKSkhg7dmzcf//9ERHRqlWrmDRpUhx//PHbvBYAaAmElgBAYpVf+XfWrFnx17/+tfGKqSeHHnpoZtGRJUuW1DqDtKSkJO64447M+yOPPLJB66tO27ZtM6+Li4trbT9ixIhMyPTCCy/E66+/3mC1lZeXlxfDhw/PvF+0aFGlNnW9lvpWW43lF5568MEHq7yGumrsay5TfpblPvvsEy+//HK8+OKLWW3HHntslf1sK9/73vfinnvuiYjSEPj222+Pk046aZvXAQAthdASAEiso48+Og499NDM+5/+9KeNWE396NKlS5xyyimZ9z/84Q9rXFjnpptuitmzZ0dE6UrK3/nOdxq8xqpsv/32mdeffPJJre179uyZWWAlnU7H6NGjY9WqVVl9VklJSYVV1iNKV8TO9pbgjz76KPO6e/fulY7X9VqyVV81HnzwwTF06NCIiFi/fn1885vfjI0bN2bV78aNG6tcPbxLly6Rk1P6V/8lS5Y0SnBZXFwckyZNyryvbQGezyvf/qGHHqp1Qar6NG7cuPj973+feX/zzTfH6NGjt9nnA0BLJLQEABLtkksuybx+5ZVX4vHHH2/EaurHJZdcklmQ5913340RI0bEv//97wptSkpK4vrrr49x48Zl9p199tlVLk6zLZS/nfnJJ5/MaoXrn/3sZ9GjR4+IiPjXv/4VBx98cIVVoz/v448/jmuvvTZ23333+POf/1zh2COPPBK77bZbXH311RWe8VleUVFR3HTTTZnbdyMijjvuuBqv5ZFHHsk6EKxNfdZ44403ZsbIlClT4vDDD4+XXnqp2s9+991348orr4y+fftWeUt5Xl5e9O/fPyJKw8OHH344q2saO3ZsZkGcrR17Zc/1jCidqThq1Kg6nf/lL3858zzXdevWxX333bdV9WTr0ksvjWuvvTbz/le/+lV8//vf3yafDQAtWevGLgAAoCYjRoyIQw45JBPYXHnllRVuE61K+XAkG+3bt49f/epXW1VnXeyyyy5x2223xemnnx6bN2+OGTNmxO677x5f/OIXY5dddok1a9bEc889V2EW4ODBg7dpjZ938MEHR+/eveOjjz6KBQsWxB577BHDhw+Pbt26ZZ6XeNBBB1WYRbrjjjvGI488Escff3wsXbo03nnnnRgxYkT07NkzDj744Nhhhx2iuLg4li5dGm+88UZ8+OGHNdbwwQcfxA9/+MP44Q9/GDvttFMMHDgwM0tx4cKF8eKLL8ayZcsy7U8//fQKM3XLHHfccdGuXbtYv359zJo1K/bcc88YOnRodOnSJXMtw4cPr3ALd7bqq8a99947/vSnP8Upp5wS69ati5deeikGDx4cu+yyS+y///7RtWvX2LBhQyxevDj+9a9/ZTVj9MQTT4yf//znmc+dOHFi7LrrrpGbm5tpc/XVV9f5mrNV/pbuww8/PHr37l2n89u1axdf/epX46677sr0961vfava9vXxPTB58uS44oorMu8LCgpi3rx5cc4552TV5xVXXBFdu3bNugYAoJw0AMBWOuKII9IRkY6I9KWXXlptu7I2EZF+5plnsu7/0UcfrXDuU089VWMNdd06d+5c94v+nDFjxmT6u+OOO7I6529/+1u6oKCg1vpGjRqVXrt2bY19lW/fUP72t7+l27RpU22dY8aMqfK8uXPnpo866qisfx8FBQXpxx9/vEIf9913XzqVSmV1fk5OTvoHP/hBeuPGjdVeyy233FJjfzWN4+rUd43pdDo9a9as9AEHHJD1z65v377p1157rcq+VqxYkd5jjz1qPP/zyo/rPn361PlnUmbp0qUVxs7vf//7LernySefzPSRSqXS//73vyscr+/vgTvuuGOL+4uI9IcffrhF1wkApNNmWgIAiXf88cfHgQceGDNnzoyI0tlLRx11VCNXtfX+67/+K95///24/fbb4+9//3u8+eabsXTp0mjXrl3suOOOMWzYsBg9enSF1aQb03/913/FzJkz4+abb47nn38+5s+fH2vWrIl0Ol3jeX369ImnnnoqZsyYEffdd19Mnz49Pvroo1i+fHm0bt06tt9+++jfv38ceOCBMXz48Bg6dGhmEZ8yJ510UixYsCCefPLJ+Mc//hGvv/56/Pvf/44VK1ZERETnzp1jt912iy984QsxevToGDBgQI01fe9734t99tknfve738VLL70Un3zySaxbt67Wa6lJfdcYEbHvvvvGzJkz48knn4yHH344/vGPf8Snn34aK1asiLy8vNhhhx1i9913j0MOOSRGjBgRQ4YMqXal8M6dO8crr7wSv/nNb+LRRx+Nt99+O1asWLFNnm/5pz/9KXMbfl5e3hYvYHPkkUdGjx49YsGCBZFOp+POO++ssGAXANB8pNJb8zczAAAAAIB6ZiEeAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJErrxi6gqSgpKYlPP/00OnXqFKlUqrHLAQAAAIAmJZ1Ox+rVq2PHHXeMnJya51IKLbP06aefRu/evRu7DAAAAABo0j766KPo1atXjW2Ellnq1KlTRJT+UPPz8xu5moZRXFwcTz75ZAwfPjxyc3MbuxxoUMY7LYWxTktivNOSGO+0JMY7LUlzH++rVq2K3r17Z3K2mggts1R2S3h+fn6zDi3bt28f+fn5zfIPBpRnvNNSGOu0JMY7LYnxTktivNOStJTxns2jFy3EAwAAAAAkitASAAAAAEgUoSUAAAAAkChCSwAAAAAgUYSWAAAAAECiCC0BAAAAgEQRWgIAAAAAidK6sQtoCdLpdBQXF0dJSUljl1Kj4uLiaN26dWzYsCE2b97c2OU0a61atYrc3NzGLgMAAAAgkYSWDWjjxo2xePHiWLduXZMIAdPpdBQWFsZHH30UqVSqsctp9vLy8qJbt26Rn5/f2KUAAAAAJIrQsoGsW7cuPvroo2jVqlVst9120a5du2jVqlWiw8CSkpJYs2ZNdOzYMXJyPDmgoZTNvF25cmV88sknERGCSwAAAIByhJYNZOnSpZGbmxt9+vSJVq1aNXY5WSkpKYmNGzdG27ZthZYNrF27dtGpU6f4+OOPY+nSpUJLAAAAgHIkUw1g06ZNsXbt2ujatWuTCSzZ9lKpVHTu3DmKioqiuLi4scsBAAAASAyhZQPYtGlTRJQ+sxBqUrYYT1N45ikAAADAtiK0bEBJfn4lyWCMAAAAAFQmtAQAAAAAEkVoCQAAAAAkitASAAAAAEgUoSWNqm/fvpFKpWLixImZfRMnToxUKlVhy8nJifz8/Nhvv/1i/PjxsWTJkmr7/Py51W3Tpk1r+AsEAAAAoM5aN3YBUJ0OHTrESSedFBGlq2vPmzcvZsyYEbNmzYo77rgjnnvuuejfv3+1548YMSIKCwurPV7TMQAAAAAaj9CSxOrWrVuFGZgREW+++WYcccQRsWjRojj//PPj0Ucfrfb8iy66KIYOHdqwRQIAAABQ79weTpOy1157xbhx4yIiYsqUKVFUVNTIFQEAAABQ34SWNDkDBw6MiIji4uJYtmxZI1cDAAAAQH0TWtLkrFq1KiIiWrVqFd26dWvkagAAAACob0JLmpyy51gee+yxkZub28jVAAAAAFDfLMTTWA48MGLhwsauooJUROSn05FKpWpuWFgYMXPmNqmpTNnq4bfeemtMmjQp+vTpEzfccEON5wwbNqzaY507d44VK1bUc5UAAAAA1AehZWNZuDDik08au4oKUv+3JcW8efOqDFAPPvjgePLJJ6Nz5841nj9ixIgoLCys8lj79u3rpUYAAAAA6p/QsrFUE6Y1pnREpP9vpmWN4eU2qr1Dhw5x0kknRUREUVFRvP322/H666/Hyy+/HN/97nfj3nvvrfH8iy66KIYOHboNKgUAAACgPgktG8s2vr06G+mSkli1alXk5+dHKqfxH3farVu3mDhxYoV9Dz74YJxyyinx5z//OQ4//PD4wQ9+0DjFAQAAANBgGj+Zgjr42te+FhdddFFERFxyySWxcuXKRq4IAAAAgPomtKTJGT9+fPTo0SM+++yz+PWvf93Y5QAAAABQz4SWNDnt27ePn/zkJxERcd1118Xy5csbuSIAAAAA6pNnWtIknXnmmXHNNdfEBx98EFdffXX87Gc/q9TmF7/4RaVnYpZ32mmnxfDhwxuwSgAAAAC2hNCSJik3Nzd++tOfxqhRo+LGG2+McePGxfbbb1+hzRNPPFFjH4MGDRJaAgAAACSQ0JJGNXfu3Er7xo4dG2PHjq313FNPPTVOPfXUSvvT6XQ9VAYAAABAY/FMSwAAAAAgUYSWAAAAAECiCC0BAAAAgEQRWgIAAAAAiSK0BAAAAAASRWgJAAAAACSK0BIAAAAASJQmGVrecsstMXDgwMjPz4/8/PwYMmRIPPbYYzWec99998Uee+wRbdu2jX322ScmT568jaoFAAAAAOqiSYaWvXr1il/84hfx6quvxsyZM+PII4+Mr3zlK/Hmm29W2f6FF16IUaNGxbe//e147bXXYuTIkTFy5Mh44403tnHlAAAAAEBtmmRo+aUvfSmOP/746N+/f+y2227xs5/9LDp27Bgvvvhile2vv/76OPbYY+OHP/xh7LnnnnHllVfG/vvvHzfddNM2rhwAAAAAqE3rxi5ga23evDnuu+++WLt2bQwZMqTKNjNmzIhx48ZV2DdixIh4+OGHq+23qKgoioqKMu9XrVoVERHFxcVRXFxcY03FxcWRTqejpKQkSkpKsrySxpdOpzP/bUp1N2UlJSWRTqejuLg4WrVq1djltChlf45r+/MMTZ2xTktivNOSGO+0JMY7LUlzH+91ua4mG1rOnj07hgwZEhs2bIiOHTvGQw89FAMGDKiy7cKFC6OgoKDCvoKCgli4cGG1/U+YMCEuv/zySvuffPLJaN++fY21tW7dOgoLC2PNmjWxcePGLK4mWVavXt3YJbQYGzdujPXr18f06dNj06ZNjV1OizRlypTGLgG2CWOdlsR4pyUx3mlJjHdakuY63tetW5d12yYbWu6+++4xa9asWLlyZdx///0xZsyYePbZZ6sNLutq/PjxFWZnrlq1Knr37h3Dhw+P/Pz8Gs/dsGFDfPTRR9GxY8do27ZtvdSzLaTT6Vi9enV06tQpUqlUY5fTImzYsCHatWsXhx9+eJMaK81BcXFxTJkyJY455pjIzc1t7HKgwRjrtCTGOy2J8U5LYrzTkjT38V52J3M2mmxo2aZNm9h1110jIuKAAw6IV155Ja6//vr43e9+V6ltYWFhLFq0qMK+RYsWRWFhYbX95+XlRV5eXqX9ubm5tQ6azZs3RyqVipycnMjJaTqPDS27JbysdhpeTk5OpFKprMYVDcPPnpbCWKclMd5pSYx3WhLjnZakuY73ulxTs0mmSkpKKjyDsrwhQ4bE1KlTK+ybMmVKtc/AZNvp27dvpFKpmDhxYmbfxIkTI5VKVdhycnIiPz8/9ttvvxg/fnwsWbKk2j4/f25127Rp0+pUa1ldY8eO3bKLBQAAACArTXKm5fjx4+O4446LnXbaKVavXh2TJk2KadOmxRNPPBEREaNHj46ePXvGhAkTIiLivPPOiyOOOCKuueaaOOGEE+Lee++NmTNnxq233tqYl0EtOnToECeddFJElM5enTdvXsyYMSNmzZoVd9xxRzz33HPRv3//as8fMWJEjbNpP3+s7Jb4sgWJAAAAAGgcTTK0XLx4cYwePToWLFgQnTt3joEDB8YTTzwRxxxzTEREzJ8/v8LtzYceemhMmjQpLr744vjxj38c/fv3j4cffjj23nvvxroEstCtW7cKMzAjIt5888044ogjYtGiRXH++efHo48+Wu35F110UQwdOrRhiwQAAACg3jXJ0PIPf/hDjceruu335JNPjpNPPrmBKmJb2WuvvWLcuHHxv//7vzFlypQoKiqq8tmjAAAAADRdzeaZlrQcAwcOjIjSFbWWLVu21f1ddtllFVZL//yzL+fOnVvpnLVr18b48eNj1113jby8vCgsLIwxY8bEJ598stX1AAAAALR0TXKmJS3bqlWrIiKiVatW0a1bt63ub9CgQTFmzJi48847IyJizJgxFY537NixwvuVK1fGoYceGvPnz48vfvGLsffee8eMGTPirrvuimeffTZef/316Ny581bXBQAAANBSCS1pcsqeY3nsscdGbm7uVvc3cuTIGDlyZCa0/PxzND/v4YcfjhEjRsRzzz0X+fn5ERGxfPnyOPLII2PWrFnxm9/8JsaPH7/VdQEAAAC0VELLRnLggQfGwoULG7uMStLpdIVbpatSWFgYM2fO3EYVlSpbPfzWW2+NSZMmRZ8+feKGG26o8Zxhw4ZVe6xz586xYsWKLaqlQ4cOcccdd2QCy4iI7bbbLi666KI49dRT46mnnhJaAgAAAGwFoWUjWbhwoecf1mLevHlVBqgHH3xwPPnkk7Xegj1ixIgoLCys8lj79u23uK4DDzwwevToUWn/nnvuGRHh9woAAACwlYSWjaS6MK2xZTvTclvo0KFDnHTSSRERUVRUFG+//Xa8/vrr8fLLL8d3v/vduPfee2s8/6KLLoqhQ4fWe1077bRTlfvLZl5u2LCh3j8TAAAAoCURWjaSbX17dTZKSkpi1apVkZ+fHzk5jb+wfLdu3So9X/LBBx+MU045Jf785z/H4YcfHj/4wQ+2eV1J+NkAAAAANGfSF5qUr33ta3HRRRdFRMQll1wSK1eubOSKAAAAAKhvQkuanPHjx0ePHj3is88+i1//+tf11m/ZSuSbNm2qtz4BAAAAqDuhJU1O+/bt4yc/+UlERFx33XWxfPnyeum3V69eERHx5ptv1kt/AAAAAGwZz7SkSTrzzDPjmmuuiQ8++CCuvvrq+NnPflapzS9+8YtKz8Qs77TTTovhw4dn3p944olx9dVXx9FHHx1HHnlkdOrUKSIifvnLX8b2229f79cAAAAAQNWEljRJubm58dOf/jRGjRoVN954Y4wbN65SsPjEE0/U2MegQYMqhJZXXnll5OTkxIMPPhgPP/xwbNy4MSIiLr74YqElAAAAwDYktKRRzZ07t9K+sWPHxtixY2s999RTT41TTz210v50Or1FtbRt2zZ++ctfxi9/+csqj9dWV9++fbf4swEAAAD4D8+0BAAAAAASRWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaNqB0Ot3YJZBwxggAAABAZULLBpCTU/pj3bx5cyNXQtKVjZGyMQMAAACA0LJB5ObmRm5ubqxZs6axSyHh1q9fH61atYrc3NzGLgUAAAAgMYSWDSCVSkWnTp1i5cqVsX79+sYuh4TavHlzrFy5Mtq3bx+pVKqxywEAAABIjNaNXUBz1a1bt1i/fn3Mnz8/8vPzo1OnTtGqVatEh1MlJSWxcePG2LBhg9uVG1A6nY6ioqJYtmxZlJSURPfu3Ru7JAAAAIBEEVo2kFatWkXv3r1j6dKlsXr16lixYkVjl1SrdDod69evj3bt2iU6XG0uOnToEIWFhdGmTZvGLgUAAAAgUYSWDahVq1ZRUFAQ3bt3j+Li4igpKWnskmpUXFwc06dPj8MPP9wzFhtY69ato3Vrf/wAAAAAqiI12QZSqVSTmE3XqlWr2LRpU7Rt21ZoCQAAAECj8eBCAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoQksAAAAAIFGElgAAAABAoggtAQAAAIBEEVoCAAAAAIkitAQAAAAAEkVoCQAAAAAkitASAAAAAEgUoSUAAAAAkChCSwAAAAAgUYSWAAAAAECiCC0BAAAAgEQRWgIAAAAAiSK0BAAAAAASRWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoQksAAAAAIFGElgAAAABAoggtAQAAAIBEEVoCAAAAAIkitAQAAAAAEkVoCQAAAAAkitASAAAAAEgUoSUAAAAAkChCSwAAAAAgUYSWAAAAAECiCC0BAAAAgEQRWgIAAAAAiSK0BAAAAAASRWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoTTK0nDBhQhx00EHRqVOn6N69e4wcOTLeeeedGs+ZOHFipFKpClvbtm23UcUAAAAAQLaaZGj57LPPxtlnnx0vvvhiTJkyJYqLi2P48OGxdu3aGs/Lz8+PBQsWZLZ58+Zto4oBAAAAgGy1buwCtsTjjz9e4f3EiROje/fu8eqrr8bhhx9e7XmpVCoKCwsbujwAAAAAYCs0ydDy81auXBkREV27dq2x3Zo1a6JPnz5RUlIS+++/f/z85z+Pvfbaq8q2RUVFUVRUlHm/atWqiIgoLi6O4uLieqo8Wcquq7leH5RnvNNSGOu0JMY7LYnxTktivNOSNPfxXpfrSqXT6XQD1tLgSkpK4stf/nKsWLEinn/++WrbzZgxI957770YOHBgrFy5Mq6++uqYPn16vPnmm9GrV69K7S+77LK4/PLLK+2fNGlStG/fvl6vAQAAAACau3Xr1sVpp50WK1eujPz8/BrbNvnQ8vvf/3489thj8fzzz1cZPlanuLg49txzzxg1alRceeWVlY5XNdOyd+/esXTp0lp/qE1VcXFxTJkyJY455pjIzc1t7HKgQRnvtBTGOi2J8U5LYrzTkhjvtCTNfbyvWrUqunXrllVo2aRvDz/nnHPi73//e0yfPr1OgWVERG5ubuy3337x/vvvV3k8Ly8v8vLyqjyvOQ6a8lrCNUIZ452WwlinJTHeaUmMd1oS452WpLmO97pcU5NcPTydTsc555wTDz30UDz99NPRr1+/OvexefPmmD17dvTo0aMBKgQAAAAAtlSTnGl59tlnx6RJk+KRRx6JTp06xcKFCyMionPnztGuXbuIiBg9enT07NkzJkyYEBERV1xxRQwePDh23XXXWLFiRVx11VUxb968OPPMMxvtOgAAAACAyppkaHnLLbdERMTQoUMr7L/jjjti7NixERExf/78yMn5z0TS5cuXx1lnnRULFy6M7bbbLg444IB44YUXYsCAAduqbAAAAAAgC00ytMxm7aBp06ZVeH/ttdfGtdde20AVAQAAAAD1pUk+0xIAAAAAaL6ElgAAAABAoggtAQAAAIBEEVoCAAAAAIkitAQAAAAAEkVoCQAAAAAkitASAAAAAEgUoSUAAAAAkChCSwAAAAAgUYSWAAAAAECiCC0BAAAAgEQRWgIAAAAAiSK0BAAAAAASRWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoQksAAAAAIFGElgAAAABAoggtAQAAAIBEEVoCAAAAAIkitAQAAAAAEkVoCQAAAAAkitASAAAAAEgUoSUAAAAAkChCSwAAAAAgUYSWAAAAAECiCC0BAAAAgEQRWgIAAAAAiSK0BAAAAAASRWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoQksAAAAAIFGElgAAAABAoggtAQAAAIBEEVoCAAAAAIkitAQAAAAAEqV1YxcAAAAAQAuXTlfcSkqqft0cjtXQLrVxY+z42msRgwZF9OnT2L+VRiW0BAAAgG0pSeHJNg5ktuRYatOm6PP665H69NOInJwmfz1qruYYEVEa1B0UEZsOPVRo2dgFAAAAkKV16yLmzInU7Nmxy7PPRs6cOf8JcVpSuNGUr4c6ax0Rgxq7CNjWBLlCSwAAgMRZvjzi7bdLt7fe+s/refMi0uloHRF7N3aNQNOQkxORSv1nK/8+22P10ce2PtZE69pcUhJvzZkTewwY0Ngjp9EJLQEAABpDOh2xcGHlYPKttyIWLWrs6pqepAUTzTBMaaxjm0pKYvYbb8Q+++4brVu3TkxdTeLnTJNTUlwc/548OfbYZZfGLqXRCS0BAAAaUklJxNy5/wkly4eUK1dm309+fsSee0bsuWds3m23eO2zz2LQgQdG69zcphmm1Ocx4Uyzli4ujvmTJ8fexx8fkZvb2OUA24jQEgAAoD5s3Bjx/vuVZ06+807E+vXZ99O9e2k4OWBAJqSMPfeM2HHHTDhXUlwcn0yeHPsKcQBopoSWAAAAdbF2bcScOZVnTn7wQcSmTdn306dPxVCyLKTs2rXhageAJkJoCQAAUJVly6pfDCdbrVpF7Lpr5WBy990jOnZsuNoBoIkTWgIAAC1XOh2xYEHFULIspFy8OPt+2rYtDSI/f0t3//4Rbdo0XP0A0EwJLQEAgOZv8+aKi+GUDylXrcq+n86dq76lu0+f0lmVAEC9EFoCAADNx8aNEe+9V/ViOBs2ZN9PQUHVi+H06GGlagDYBoSWAABA07NmTfWL4WzenH0/fftWPXNyu+0arHQAoHZCSwAAILk++6zqW7rnz8++j9atq18Mp0OHhqsdANhiQksAAKBxpdMRn35a9WI4S5Zk30+7dlUvhrPrrhbDAYAmRmgJAABsG5s3R3z4YeVgcs6cui2G06VL9Yvh5OQ0WPkAwLYjtAQAAOpXUVH1i+EUFWXfT2Fh1YvhFBZaDAcAmjmhJQAAsGVWr656MZx//zv7xXBSqaoXw9ljD4vhAEALJrQEAABqtnRp1YvhfPRR9n20bh3Rv3/Vi+G0b99wtQMATZLQEgAAKF0M55NPql4MZ+nS7Ptp1650lmRVi+Hk5jZc/QBAsyK0BACAlmTz5tLbt6taDGf16uz76dKlYjBZ9nqnnSyGAwBsNaElAAA0R0VFEe++W/mW7nffrdtiOD16VL0YTkGBxXAAgAYjtAQAgKZs9eqKsybLL4ZTUpJdH2WL4Xx+5uQee5TOqAQA2MaElgAA0BQsWVL1Yjgff5x9H7m5VS+Gs9tuFsMBABJFaAkAAEmRTpeGkFUthvPZZ9n307591Yvh7LKLxXAAgCZBaAkAANvapk0VF8MpCynnzIlYsyb7frbbrurFcHr3thgOANCkCS0BAKChbNhQ/WI4Gzdm38+OO1a9GE737hbDAQCaJaElAABsrVWrql4M58MP67YYTr9+VS+G07lzw9YPAJAwQktKFRX5V3oAgJqk09UvhvPJJ9n3k5tbuvBNVYvhtGvXcPUDADQhQktKnX56tP773+OY/Pxo1adPRI8eEQUF/9kKCyu+79xZyAkANE/pdMRHH1UOJt96K2LZsuz76dCh+sVwWvtrOABATZrk35YmTJgQDz74YMyZMyfatWsXhx56aPzyl7+M3Xffvcbz7rvvvvjJT34Sc+fOjf79+8cvf/nLOP7447dR1Qm3aFGkioqi/ZIlpTMIapOXVzHErC7cLCiI6NJFwAkAJM+mTREffFD5lu45cyLWrs2+n65dq14Mp1cvi+EAAGyhJhlaPvvss3H22WfHQQcdFJs2bYof//jHMXz48HjrrbeiQ4cOVZ7zwgsvxKhRo2LChAnxX//1XzFp0qQYOXJk/POf/4y99957G19BAu26a6SXL4+NH38cbVatilQ6XXP7oqKI+fNLt9q0aVN7uFn2XsAJANS39eurXwynuDj7fnr2rBxM7rlnxA47+PsLAEA9a5Kh5eOPP17h/cSJE6N79+7x6quvxuGHH17lOddff30ce+yx8cMf/jAiIq688sqYMmVK3HTTTfHb3/62wWtOvDvuiE3FxfH45Mlx/PDhkbtyZcSiRRELF5b+t2z7/PslS0pvoarJxo2lt1h99FHtdbRpU7oKZnWzNsvv2247/wcBAPiPlStju3feidSSJRVDyg8/rP3vK2VyciovhrPnnhbDAQDYxppkaPl5K1eujIiIrl27VttmxowZMW7cuAr7RowYEQ8//HCV7YuKiqKoqCjzftWqVRERUVxcHMV1+Rf5JqTsuorT6Yjtty/dBgyo+aRNmyKWLi29vXzx4or/XbQoYvHizH9jyZJI1bZ65saNER9/XLrVIp2bG9G9e6TLQszu3SPdvXtEYWHpfwsKMv+Nrl0FnFSQGe/N9M8zlDHWaXbS6dK/X8yZE6k5cyLefjvzOvfTT6Pqf76uops2bSL694/0HnuUbnvuGek99ihdDKdt26pP8ueIBPH9TktivNOSNPfxXpfrSqXT2f6zczKVlJTEl7/85VixYkU8//zz1bZr06ZN3HnnnTFq1KjMvt/85jdx+eWXx6JFiyq1v+yyy+Lyyy+vtH/SpEnRvn37+im+pdm8OfJWr468FStKt+XLI2/lyshbsSLalu0r21atqj3grIOS1q2jqHPn0q1Llyjq0iU2bLddhfdFXbrEhi5dorhjR8+fAoDGVlIS7ZYujU4ffRSdPv44On78cXT6+OPo9NFH0WbNmqy72dS2bazu1StW9+4da3r1Kn3dq1esKyyMdKtWDXgBAAB83rp16+K0006LlStXRn5+fo1tm/xMy7PPPjveeOONGgPLLTF+/PgKMzNXrVoVvXv3juHDh9f6Q22qiouLY8qUKXHMMcdEbm5uo9ayafPmiM8++8+MzRpmcsbixbUGnDmbNkW7zz6Ldp99Vutnp1u3Lr1FvdwszgozN8vN5IyuXQWcTVSSxjs0JGOdxCsujvjgg8xsydT/zZyMd96J1Lp1WXeT3n77KNljj/ioQ4fY8aijImfvvUtnTvbqFR1TqejYgJcAjcH3Oy2J8U5L0tzHe9mdzNlo0qHlOeecE3//+99j+vTp0atXrxrbFhYWVppRuWjRoigsLKyyfV5eXuTl5VXan5ub2ywHTXmJuMbc3NKH3ffsWXvbkpLSgLO252+WhZybN9fYXWrTpohPP4349NOo9Yby1q1LH75f2/M3CwpKb7cXcCZOIsY7bAPGOo1u/fqId96pvBjOe+/V7bbrXr2qXAwntcMOUVJcHK9Pnhw9jz8+WhvvtBC+32lJjHdakuY63utyTU0ytEyn0/Hf//3f8dBDD8W0adOiX79+tZ4zZMiQmDp1apx//vmZfVOmTIkhQ4Y0YKVsEzk5pcHhDjtE7LNPzW3LAs7aws2FC7MKOGPTpogFC0q32rRq9Z9FhmpbSV3ACUBTtWLFfwLJsu2ttyLmzq3bYjg771z1YjjN9I4XAAAqapKh5dlnnx2TJk2KRx55JDp16hQLFy6MiIjOnTtHu3btIiJi9OjR0bNnz5gwYUJERJx33nlxxBFHxDXXXBMnnHBC3HvvvTFz5sy49dZbG+06aATlA8699665bUlJxLJlNQeb5WdwbtpUc3+bN9ct4Nxhh+qDzfL7tt++tD0AbCvpdOn//pUPJcteZ/O/c2XatInYfffKMyf7969+MRwAAFqEJhla3nLLLRERMXTo0Ar777jjjhg7dmxERMyfPz9yys1UO/TQQ2PSpElx8cUXx49//OPo379/PPzww7F3bcEVLVdOTkS3bqXbXnvV3LakJGL58trDzbItm4Bz4cLSLZs6ywLOqmZtln/frZuAE4DslZREzJ9fMZQsCylXrMi+n44dq7ylO/r1K33UCgAAfE6T/FtiNgueT5s2rdK+k08+OU4++eQGqIgWLyendMbj9ttnH3DWFm6W3aJe23O+Skr+c86//lV7neVncNY0k3OHHQScAC1FcXHE++9XDibfeSeiDovhRLdulW/pHjCg9BnVqVqfFA0AABlNMrSEJq18wDlgQM1t0+mKAWdtMznrEnBmU2e3brU/f7NsBqeZMgDJt25d9Yvh1HYXQHm9e1c9c7Jbt4arHQCAFkXKAEmWSkV07Vq67blnzW3T6dJb9bK9RX3jxpr7Kykpnem5eHHE7Nm111kWcNa2kvoOOwg4ARra8uVVL4Yzb17dFsPZZZeqF8Pp1Klh6wcAoMWTHEBzkUpFbLdd6ZZtwJlNuLlwYe0BZzodsWRJ6fbGG7XXuf32tYebBQWlq60LOAGqlk6XfkdXtRhONs9ELpOXV/1iOHl5DVc/AADUQBoALVH5gHOPPWpum05HrFxZ+/M3y14XFdXe39KlpVu2AWdtz98sLCydwZmbW7efA0BTUFJSOkPy84vhvP123RbD6dSp+sVwPMMYAICEEVoCNUulIrp0Kd12373mtul0xKpV2T1/c9GiiA0bau+vLOB8883aay0fcNY0k7N792yvHmDb2bix+sVw1q/Pvp8ddqh6MZwdd7QYDgAATYbQEqg/qVRE586l22671dw2nY5YvbrmWZvl99UWcEZEfPZZ6fbWW7U2bd21awzr0CFaXXddxYCzqlvU27TJ7voBsrF2bdWL4bz/ft0Ww9lpp6pnTm6/fcPVDgAA24jQEmgcqVREfn7plm3AmU24uWhRVjOSUsuWRf6yZREffVR7rV271v78zcJCASdQ0bJl1S+Gk61WrapfDKdjx4arHQAAGpnQEki+8gFn//41t02nI9asqTXcTC9aFJs//TRa1/YMzojS4KEsfKjNdttlf4u6BS6g6UunIxYsqHoxnEWLsu8nL680iPz8Ld277uq7AgCAFkloCTQvqVTpYhOdOpX+n/1qbCoujsmTJ8fxhx8euZ99VvvzNxcujFi3rvbPX768dJszp/a2XbrUHm6WbUILaFybN1e/GM7Kldn3k59f9S3dfftaDAcAAMoRWgItW8eOpbMjawg4M8rP4KztFvW1a2vvb8WK0u2dd2pvWxZw1raSekFBRNu2tfcHVG3jxoj33qt6MZxsnq1bpnv3qhfD6dHDYjgAAJAFoSVAtjp2LN122aX2tmvXZhduLlpUGobWpi4BZ+fOtT9/U8BJS7d2bemM6KoWw9m8Oft++vSpeuZk164NVzsAALQAQkuAhtChQ8TOO5dutVm3Lrtwc9Gi0gWJarNyZen27ru1t83Pz/4W9Xbtau8PkmbZssq3dL/1VsT8+dn30apV6WzszweTu+9uMRwAAGggQkuAxta+fUS/fqVbbcoCzmxuU88m4Fy1qnR7773a23bqlF24WVBQek2wraTTEZ9+WvViOIsXZ99P27bVL4bTpk3D1Q8AAFQitARoSuoScK5fX/viQmWvV62qvb/Vq0u3bAPO2oLNsn0CTrK1eXPE3LmVg8m3385uDJfp3LnqW7r79LEYDgAAJITQEqC5ateudEXivn1rb7t+femMtGxuUc9mpeSygPP992tv27FjduFmQUHpbfc0f0VFVS+G8+67dVsMp6Cg6sVwCgsthgMAAAkntASgNODs06d0q82GDZWDzepuU1+xovb+1qwp3T74oPa2HTpkf4u6Zw0m35o1VS+G88EHdVsMp2/fqmdObrddg5UOAAA0LKElAHXTtm3dAs7Fi7NbST2bgHPt2tJAK9uAM5tws7BQwNnQPvus6sVwPvoo+z5at65+MRwzcAEAoNkRWgLQcNq2jdhpp9KtNkVF/wk4a3r+5qJFEcuX197f2rUR//536Vab9u2zv0W9Y0e3FlclnY745JOqF8NZsiT7ftq1q3oxnF12sRgOAAC0IEJLAJIhLy+id+/SrTblA87ablFftqz2/tati/jww9KtNu3aZRduFhSULkjU3ALOzZtLf05VLYaTzYr1Zbp0qX4xnJycBisfAABoGoSWADQ9dQk4N26sHHBWd5t6NgHn+vWlK1jPnVt727KAM5tb1JMWcBYVlS58U9ViOEVF2fdTWFg5mNxzT4vhAAAANRJaAtC8tWkT0atX6VabjRtLb2Wu7fmbixaVPqexNnUJONu2zS7cLCiIyM+vv8Bv9erqF8MpKcmuj1Sq4mI4ZSHlHntYDAcAANgiQksAKNOmTUTPnqVbbYqLSwPO2sLNRYsili6tvb8NGyLmzSvdapOXV324+fn37duXnrN0acR771WeOfnxx7V/XpnWrSP69696MZyyzwEAAKgHQksA2BK5uRE77li61aYs4MzmFvXPPitd1KYmRUUR8+eXbrVonZcXx7VuHblr12Z5YVF6W3v5WZPlF8PJzc2+HwAAgC0ktASAhlaXgHPTpooBZ00zOZcurTXgTBUVRZvqnkG53XZVL4az004WwwEAABqV0BIAkqR164gePUq32mzaVBpcVhVu/t/79MKFsW7x4mi3116Rs9deFWdPFhRYDAcAAEgkoSUANFWtW5c+w7KwMGLffatssqm4OJ6aPDmOP/74yHFrNwAA0ES49wsAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoQksAAAAAIFGElgAAAABAoggtAQAAAIBEEVoCAAAAAIkitAQAAAAAEkVoCQAAAAAkitASAAAAAEgUoSUAAAAAkChCSwAAAAAgUYSWAAAAAECiCC0BAAAAgERpvSUnrVq1KiIi8vPzt+rD16xZE08//XRERHz5y1/eqr4AAAAAgOZhi0LLLl26RE5OTvzrX/+KAQMGVDr+6aefxsUXXxypVCr+8Ic/VNvPvHnzYuTIkZGTkxObNm3aklIAAAAAgGZmi28PT6fT1R5bvnx5TJw4MSZOnLjVfQEAAAAALYtnWgIAAAAAiSK0BAAAAAASRWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKJsVWiZSqXqqw4AAAAAgIiIaL01J++9997VHisLNFu1arU1HwEAAAAAtDBbFVqm0+n6qgMAAAAAICK2MLQ8/PDD3RoOAAAAADSILQotp02bVs9lAAAAAACUsno4AAAAAJAoQksAAAAAIFEaPbRct25dXHPNNY1dBgAAAACQEI0WWq5evTp+9rOfRd++fePCCy9srDIAAAAAgITZooV4tsayZcvi2muvjZtvvjlWrlwZ6XTaSuQAAAAAQMZWzbScN29enHvuuTFgwIDo1KlTdO3aNfbff/+YMGFCrFy5skLbNWvWxKWXXhp9+/aNn//857FixYpIp9PRrVu3+OlPf7pVFwEAAAAANB9bPNNyypQpceKJJ8batWsjIiKdTkdExOuvvx6vv/563HXXXfHMM89EYWFh/OMf/4jTTz89Pvroo0y7nj17xv/8z//Ed77znWjXrl09XAoAAAAA0BxsUWi5ZMmSGDVqVKxZsyazr0OHDtG6devMDMt33303zj777DjvvPNixIgRsXHjxkin09GvX7+46KKLYuzYsZGbm1s/VwEAAAAANBtbdHv473//+1i2bFmkUqk46aST4v3334/Vq1fH8uXL49NPP41zzjknIiIeeeSR+MY3vhFFRUXRsWPHuPHGG+Odd96Js846S2AJAAAAAFRpi2ZaPvnkkxERMXjw4PjLX/5S4VhhYWHccMMNsXr16rjzzjvj448/ji5dusRzzz0Xe+2119ZXDAAAAAA0a1s003LOnDmRSqXiBz/4QbVtzj333IiISKVSce655wosAQAAAICsbFFouXz58oiI2HXXXatt079//8zrL37xi1vyMQAAAABAC7RFoWVxcXFERHTq1KnaNh07dsy8Liws3JKPAQAAAABaoC0KLesqlUpti48BAAAAAJqBbRJaAgAAAABka4tWDy9zxhlnRIcOHba6XSqViqlTp25NKQAAAABAM7FVoeXMmTNrPF52W3hN7dLptNvHAQAAAICMLQ4t0+l0fdYBAAAAABARWxhalpSU1HcdAAAAAAARYSEeAAAAACBhhJYAAAAAQKIILQEAAACARNmiZ1peccUV9V1HXHLJJVm3nT59elx11VXx6quvxoIFC+Khhx6KkSNHVtt+2rRpMWzYsEr7FyxYEIWFhVtSLgAAAADQQLYotLzssssilUrVayF1CS3Xrl0b++67b3zrW9+Kr33ta1mf984770R+fn7mfffu3etUIwAAAADQ8LYotIyISKfT9VZEXQPQ4447Lo477rg6f0737t2jS5cudT4PAAAAANh2tii0fOaZZ+q7jm1i0KBBUVRUFHvvvXdcdtllcdhhh1XbtqioKIqKijLvV61aFRERxcXFUVxc3OC1Noay62qu1wflGe+0FMY6LYnxTktivNOSGO+0JM19vNflulLp+pwy2QhSqVStz7R85513Ytq0aXHggQdGUVFR3HbbbXH33XfHSy+9FPvvv3+V51x22WVx+eWXV9o/adKkaN++fX2VDwAAAAAtwrp16+K0006LlStXVniEY1VaRGhZlSOOOCJ22mmnuPvuu6s8XtVMy969e8fSpUtr/aE2VcXFxTFlypQ45phjIjc3t7HLgQZlvNNSGOu0JMY7LYnxTktivNOSNPfxvmrVqujWrVtWoeUWP9OyqTv44IPj+eefr/Z4Xl5e5OXlVdqfm5vbLAdNeS3hGqGM8U5LYazTkhjvtCTGOy2J8U5L0lzHe12uKacB60i0WbNmRY8ePRq7DAAAAADgc5rkTMs1a9bE+++/n3n/4YcfxqxZs6Jr166x0047xfjx4+OTTz6Ju+66KyIirrvuuujXr1/stddesWHDhrjtttvi6aefjieffLKxLgEAAAAAqEaTDC1nzpwZw4YNy7wfN25cRESMGTMmJk6cGAsWLIj58+dnjm/cuDH+3//7f/HJJ59E+/btY+DAgfHUU09V6AMAAAAASIYmGVoOHTo0alo/aOLEiRXeX3jhhXHhhRc2cFUAAAAAQH1osc+0BAAAAACSSWgJAAAAACSK0BIAAAAASBShJQAAAACQKEJLAAAAACBRhJYAAAAAQKIILQEAAACARBFaAgAAAACJIrQEAAAAABJFaAkAAAAAJIrQEgAAAABIFKElAAAAAJAoQksAAAAAIFGElgA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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7acf3a7f-4dd8-44df-b201-509aa4caa7f0",
+      "metadata": {
+        "id": "7acf3a7f-4dd8-44df-b201-509aa4caa7f0"
+      },
+      "source": [
+        "### 11) Визуализировали элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a2fbf3a9-5e11-49d0-a0db-f77f3edeb7b7",
+      "metadata": {
+        "id": "a2fbf3a9-5e11-49d0-a0db-f77f3edeb7b7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "9754e63f-1a64-44d1-ab92-334570f7019e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# построение областей аппроксимации и точек тестового набора\n",
+        "lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bd63edf4-3452-41db-8080-d8a2ff4f09a4",
+      "metadata": {
+        "id": "bd63edf4-3452-41db-8080-d8a2ff4f09a4"
+      },
+      "source": [
+        "### 12) Результаты исследования занесли в таблицу:\n",
+        "Табл. 1 Результаты задания №1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "|      | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | Значение показателя<br>Excess | Значение показателя<br>Approx | Количество обнаруженных<br>аномалий |\n",
+        "|-----:|------------------------------|----------------------------------------|-----------------------------|--------------------|-------------------------------|-------------------------------|--------------------------------|-------------------------------------|\n",
+        "| AE1  | 1                            | 1                            | 1000                        | 19.5568             | 6.53                         | 12.67                          | 0.073                           | 0                                   |\n",
+        "| AE2  | 5                            | 3,2,1,2,3                                | 3000                          | 0.0108             | 0.4                         | 0.33                          | 0.750                           | 4                                   |\n"
+      ],
+      "metadata": {
+        "id": "df2BIryKfkpk"
+      },
+      "id": "df2BIryKfkpk"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b8e54861-e2c2-4755-a2d8-5d4b6cace481",
+      "metadata": {
+        "id": "b8e54861-e2c2-4755-a2d8-5d4b6cace481"
+      },
+      "source": [
+        "### 13) Сделали выводы о требованиях к:\n",
+        "- данным для обучения,\n",
+        "- архитектуре автокодировщика,\n",
+        "- количеству эпох обучения,\n",
+        "- ошибке MSE_stop, приемлемой для останова обучения,\n",
+        "- ошибке реконструкции обучающей выборки (порогу обнаружения\n",
+        "аномалий),\n",
+        "- характеристикам качества обучения EDCA одноклассового\n",
+        "классификатора\n",
+        "\n",
+        "для качественного обнаружения аномалий в данных."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1) Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение, кол-во скрытых слоев 3-5.\n",
+        "2) Архитектура автокодировщика должна постепенно сужатся к бутылочному горлышку,а затем постепенно возвращатся к исходным выходным размерам.\n",
+        "3) В рамках данной работы оптимальное кол-во эпох 3000 с patience 100 эпох\n",
+        "4) Оптимальная ошибка MSE-stop в районе 0.01, желательно не меньше для предотвращения переобучения\n",
+        "5) Значение порога в районе 0.4\n",
+        "6) Значение Excess не больше 0.5, значение Deficit равное 0, значение Coating равное 1, значение Approx не меньше 0.7"
+      ],
+      "metadata": {
+        "id": "1s5_ye8vkleI"
+      },
+      "id": "1s5_ye8vkleI"
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/labworks/LW2/notebook с полными выводами/LR2_задание2.ipynb b/labworks/LW2/notebook с полными выводами/LR2_задание2.ipynb
new file mode 100644
index 0000000..1799e9f
--- /dev/null
+++ b/labworks/LW2/notebook с полными выводами/LR2_задание2.ipynb	
@@ -0,0 +1,1054 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "58e787ca-562f-458d-8c62-e86b6fb1580a",
+      "metadata": {
+        "id": "58e787ca-562f-458d-8c62-e86b6fb1580a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "04ef3efa-30a9-4178-a620-8e17d23b4b81"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/content/drive/MyDrive/Colab Notebooks/is_lab2/lab02_lib.py:444: SyntaxWarning: invalid escape sequence '\\X'\n",
+            "  hatch='/', label='Площадь |Xd| за исключением |Xt| (|Xd\\Xt|)')\n",
+            "/content/drive/MyDrive/Colab Notebooks/is_lab2/lab02_lib.py:452: SyntaxWarning: invalid escape sequence '\\X'\n",
+            "  facecolor='none', label='Площадь |Xt| за исключением |Xd| (|Xt\\Xd|)')\n"
+          ]
+        }
+      ],
+      "source": [
+        "# импорт модулей\n",
+        "import os\n",
+        "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')\n",
+        "\n",
+        "import numpy as np\n",
+        "import lab02_lib as lib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задание 2"
+      ],
+      "metadata": {
+        "id": "rqdVflKXo6Bo"
+      },
+      "id": "rqdVflKXo6Bo"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1) Изучить описание своего набора реальных данных, что он из себя представляет"
+      ],
+      "metadata": {
+        "id": "rSvJtTReo928"
+      },
+      "id": "rSvJtTReo928"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Бригада 6 => набор данных Cardio. Это реальный набор данных, который состоит из измерений частоты сердечных сокращений плода и\n",
+        "сокращений матки на кардиотокограммах, классифицированных экспертами\n",
+        "акушерами. Исходный набор данных предназначен для классификации. В нем\n",
+        "представлено 3 класса: «норма», «подозрение» и «патология». Для обнаружения\n",
+        "аномалий класс «норма» принимается за норму, класс «патология» принимается за\n",
+        "аномалии, а класс «подозрение» был отброшен.\n",
+        "\n",
+        "| Количество<br>признаков | Количество<br>примеров | Количество<br>нормальных примеров | Количество<br>аномальных примеров |\n",
+        "|-------------------------:|-----------------------:|----------------------------------:|-----------------------------------:|\n",
+        "| 21                       | 1764                   | 1655                               | 109                                |\n"
+      ],
+      "metadata": {
+        "id": "gf-0gJ7jqTdk"
+      },
+      "id": "gf-0gJ7jqTdk"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2) Загрузить многомерную обучающую выборку реальных данных Cardio.txt."
+      ],
+      "metadata": {
+        "id": "N2Egw1pho-F_"
+      },
+      "id": "N2Egw1pho-F_"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка обчуающей выборки\n",
+        "train = np.loadtxt('data/cardio_train.txt', dtype=float)"
+      ],
+      "metadata": {
+        "id": "G8QTxAFapASY"
+      },
+      "id": "G8QTxAFapASY",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3) Вывести полученные данные и их размерность в консоли."
+      ],
+      "metadata": {
+        "id": "zj2WXPNco-Tz"
+      },
+      "id": "zj2WXPNco-Tz"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('train:\\n', train)\n",
+        "print('train.shape:', np.shape(train))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W6hTlfk6pAo9",
+        "outputId": "6494b55a-d7f6-400c-8cfb-cf1b2aff34cf"
+      },
+      "id": "W6hTlfk6pAo9",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train:\n",
+            " [[ 0.00491231  0.69319077 -0.20364049 ...  0.23149795 -0.28978574\n",
+            "  -0.49329397]\n",
+            " [ 0.11072935 -0.07990259 -0.20364049 ...  0.09356344 -0.25638541\n",
+            "  -0.49329397]\n",
+            " [ 0.21654639 -0.27244466 -0.20364049 ...  0.02459619 -0.25638541\n",
+            "   1.1400175 ]\n",
+            " ...\n",
+            " [ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.65718941\n",
+            "   1.1400175 ]\n",
+            " [ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.62378908\n",
+            "  -0.49329397]\n",
+            " [ 1.0630827  -0.51148142 -0.16958144 ...  0.57633422 -0.65718941\n",
+            "  -0.49329397]]\n",
+            "train.shape: (1654, 21)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4) Создать и обучить автокодировщик с подходящей для данных архитектурой. Выбрать необходимое количество эпох обучения."
+      ],
+      "metadata": {
+        "id": "0T11A0x4o-gr"
+      },
+      "id": "0T11A0x4o-gr"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# **kwargs\n",
+        "# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)\n",
+        "# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)\n",
+        "# early_stopping_value = значение для ранней остановки (по умолчанию -  0.0001)\n",
+        "\n",
+        "from time import time\n",
+        "\n",
+        "patience = 4000\n",
+        "start = time()\n",
+        "ae3_v1_trained, IRE3_v1, IREth3_v1 = lib.create_fit_save_ae(train,'out/AE3_V1.h5','out/AE3_v1_ire_th.txt',\n",
+        "100000, False, patience, early_stopping_delta = 0.001)\n",
+        "print(\"Время на обучение: \", time() - start)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rPwtBtdRPztp",
+        "outputId": "a89c34e5-38fd-4d77-c9b8-a73ba7cff7d1"
+      },
+      "id": "rPwtBtdRPztp",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n",
+            "Задайте количество скрытых слоёв (нечетное число) : 7\n",
+            "Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 46 26 14 10 14 26 46\n",
+            "\n",
+            "Epoch 1000/100000\n",
+            " - loss: 0.0574\n",
+            "\n",
+            "Epoch 2000/100000\n",
+            " - loss: 0.0360\n",
+            "\n",
+            "Epoch 3000/100000\n",
+            " - loss: 0.0261\n",
+            "\n",
+            "Epoch 4000/100000\n",
+            " - loss: 0.0217\n",
+            "\n",
+            "Epoch 5000/100000\n",
+            " - loss: 0.0204\n",
+            "\n",
+            "Epoch 6000/100000\n",
+            " - loss: 0.0185\n",
+            "\n",
+            "Epoch 7000/100000\n",
+            " - loss: 0.0189\n",
+            "\n",
+            "Epoch 8000/100000\n",
+            " - loss: 0.0176\n",
+            "\n",
+            "Epoch 9000/100000\n",
+            " - loss: 0.0165\n",
+            "\n",
+            "Epoch 10000/100000\n",
+            " - loss: 0.0159\n",
+            "\n",
+            "Epoch 11000/100000\n",
+            " - loss: 0.0158\n",
+            "\n",
+            "Epoch 12000/100000\n",
+            " - loss: 0.0150\n",
+            "\n",
+            "Epoch 13000/100000\n",
+            " - loss: 0.0147\n",
+            "\n",
+            "Epoch 14000/100000\n",
+            " - loss: 0.0146\n",
+            "\n",
+            "Epoch 15000/100000\n",
+            " - loss: 0.0142\n",
+            "\n",
+            "Epoch 16000/100000\n",
+            " - loss: 0.0138\n",
+            "\n",
+            "Epoch 17000/100000\n",
+            " - loss: 0.0138\n",
+            "\n",
+            "Epoch 18000/100000\n",
+            " - loss: 0.0140\n",
+            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n",
+            "Время на обучение:  765.1605446338654\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5) Зафиксировать ошибку MSE, на которой обучение завершилось. Построить график ошибки реконструкции обучающей выборки. Зафиксировать порог ошибки реконструкции – порог обнаружения аномалий."
+      ],
+      "metadata": {
+        "id": "8ALjaY8lo-sa"
+      },
+      "id": "8ALjaY8lo-sa"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Скрытых слоев 7, нейроны: 46->26->14->10->14->26->48\n",
+        "\n",
+        "Ошибка MSE_AE3_v1 = 0.0126"
+      ],
+      "metadata": {
+        "id": "b4sk_fhYY6Qb"
+      },
+      "id": "b4sk_fhYY6Qb"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE3_v1, IREth3_v1, 'AE3_v1')"
+      ],
+      "metadata": {
+        "id": "a4sR3SSDpBPU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 724
+        },
+        "outputId": "8f487b44-e128-4e72-b3ff-e400431401f3"
+      },
+      "id": "a4sR3SSDpBPU",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6) Сделать вывод о пригодности обученного автокодировщика для качественного обнаружения аномалий. Если порог ошибки реконструкции слишком велик, то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (6)."
+      ],
+      "metadata": {
+        "id": "DGBM9xNFo-4k"
+      },
+      "id": "DGBM9xNFo-4k"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# **kwargs\n",
+        "# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)\n",
+        "# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)\n",
+        "# early_stopping_value = значение для ранней остановки (по умолчанию -  0.0001)\n",
+        "\n",
+        "from time import time\n",
+        "\n",
+        "patience = 4000\n",
+        "start = time()\n",
+        "ae3_v2_trained, IRE3_v2, IREth3_v2 = lib.create_fit_save_ae(train,'out/AE3_V2.h5','out/AE3_v2_ire_th.txt',\n",
+        "100000, False, patience, early_stopping_delta = 0.001)\n",
+        "print(\"Время на обучение: \", time() - start)"
+      ],
+      "metadata": {
+        "id": "ZvM1VbKEgalO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d5e2632a-b541-47ec-dcc4-434aacfecaf5"
+      },
+      "id": "ZvM1VbKEgalO",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n",
+            "Задайте количество скрытых слоёв (нечетное число) : 11\n",
+            "Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 48 36 28 22 16 10 16 22 28 36 48\n",
+            "\n",
+            "Epoch 1000/100000\n",
+            " - loss: 0.0554\n",
+            "\n",
+            "Epoch 2000/100000\n",
+            " - loss: 0.0276\n",
+            "\n",
+            "Epoch 3000/100000\n",
+            " - loss: 0.0217\n",
+            "\n",
+            "Epoch 4000/100000\n",
+            " - loss: 0.0195\n",
+            "\n",
+            "Epoch 5000/100000\n",
+            " - loss: 0.0179\n",
+            "\n",
+            "Epoch 6000/100000\n",
+            " - loss: 0.0166\n",
+            "\n",
+            "Epoch 7000/100000\n",
+            " - loss: 0.0157\n",
+            "\n",
+            "Epoch 8000/100000\n",
+            " - loss: 0.0151\n",
+            "\n",
+            "Epoch 9000/100000\n",
+            " - loss: 0.0144\n",
+            "\n",
+            "Epoch 10000/100000\n",
+            " - loss: 0.0139\n",
+            "\n",
+            "Epoch 11000/100000\n",
+            " - loss: 0.0135\n",
+            "\n",
+            "Epoch 12000/100000\n",
+            " - loss: 0.0131\n",
+            "\n",
+            "Epoch 13000/100000\n",
+            " - loss: 0.0131\n",
+            "\n",
+            "Epoch 14000/100000\n",
+            " - loss: 0.0128\n",
+            "\n",
+            "Epoch 15000/100000\n",
+            " - loss: 0.0117\n",
+            "\n",
+            "Epoch 16000/100000\n",
+            " - loss: 0.0119\n",
+            "\n",
+            "Epoch 17000/100000\n",
+            " - loss: 0.0108\n",
+            "\n",
+            "Epoch 18000/100000\n",
+            " - loss: 0.0103\n",
+            "\n",
+            "Epoch 19000/100000\n",
+            " - loss: 0.0100\n",
+            "\n",
+            "Epoch 20000/100000\n",
+            " - loss: 0.0100\n",
+            "\n",
+            "Epoch 21000/100000\n",
+            " - loss: 0.0094\n",
+            "\n",
+            "Epoch 22000/100000\n",
+            " - loss: 0.0090\n",
+            "\n",
+            "Epoch 23000/100000\n",
+            " - loss: 0.0090\n",
+            "\n",
+            "Epoch 24000/100000\n",
+            " - loss: 0.0098\n",
+            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n",
+            "Время на обучение:  1065.6178832054138\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Скрытых слоев 7, нейроны: 48->36->28->22->16->12->16->22->28->36->48\n",
+        "\n",
+        "Ошибка MSE_AE3_v1 = 0.0098"
+      ],
+      "metadata": {
+        "id": "9BrPUb_8fX5R"
+      },
+      "id": "9BrPUb_8fX5R"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('training', IRE3_v2, IREth3_v2, 'AE3_v2')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 724
+        },
+        "id": "kh1eMtvpf6F-",
+        "outputId": "f4226ec6-4667-4280-ef13-f87c28dc13f6"
+      },
+      "id": "kh1eMtvpf6F-",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 7) Изучить и загрузить тестовую выборку Cardio.txt."
+      ],
+      "metadata": {
+        "id": "vJvW1mOtpgFO"
+      },
+      "id": "vJvW1mOtpgFO"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#загрузка тестовой выборки\n",
+        "test = np.loadtxt('data/cardio_test.txt', dtype=float)\n",
+        "print('\\n test:\\n', test)\n",
+        "print('test.shape:', np.shape(test))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GrXQ5YymtD2u",
+        "outputId": "47e6519f-3133-4eb0-c955-64df75880f72"
+      },
+      "id": "GrXQ5YymtD2u",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " test:\n",
+            " [[ 0.21654639 -0.65465178 -0.20364049 ... -2.0444214   4.987467\n",
+            "  -0.49329397]\n",
+            " [ 0.21654639 -0.5653379  -0.20364049 ... -2.1133887   6.490482\n",
+            "  -0.49329397]\n",
+            " [-0.3125388  -0.91998844  6.9653692  ... -1.1478471   3.9186563\n",
+            "  -0.49329397]\n",
+            " ...\n",
+            " [-0.41835583 -0.91998844 -0.16463485 ... -1.4926834   0.24461959\n",
+            "  -0.49329397]\n",
+            " [-0.41835583 -0.91998844 -0.15093411 ... -1.4237162   0.14441859\n",
+            "  -0.49329397]\n",
+            " [-0.41835583 -0.91998844 -0.20364049 ... -1.2857816   3.5846529\n",
+            "  -0.49329397]]\n",
+            "test.shape: (109, 21)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 8) Подать тестовую выборку на вход обученного автокодировщика для обнаружения аномалий. Вывести график ошибки реконструкции элементов тестовой выборки относительно порога."
+      ],
+      "metadata": {
+        "id": "6iqO4Yxbpgob"
+      },
+      "id": "6iqO4Yxbpgob"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE3\n",
+        "predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1)"
+      ],
+      "metadata": {
+        "id": "Vxj1fTAikDuU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "595470a6-9824-43d6-90af-89d584c971e3"
+      },
+      "id": "Vxj1fTAikDuU",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 727
+        },
+        "id": "21DdnIKYtmtM",
+        "outputId": "2de5d71c-bd32-4899-c902-7af454334fd1"
+      },
+      "id": "21DdnIKYtmtM",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE3\n",
+        "predicted_labels3_v2, ire3_v2 = lib.predict_ae(ae3_v2_trained, test, IREth3_v2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ql1JMAM6sf7Z",
+        "outputId": "28a32237-a652-410a-d768-4088d74ac238"
+      },
+      "id": "Ql1JMAM6sf7Z",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Построение графика ошибки реконструкции\n",
+        "lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 727
+        },
+        "id": "jczBRWjTt35R",
+        "outputId": "d63185b5-9904-4e31-c77e-afc23b63f7dd"
+      },
+      "id": "jczBRWjTt35R",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)"
+      ],
+      "metadata": {
+        "id": "2kF1XknIpg48",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4a05ee5a-54d0-4995-b7b7-8190fa974b23"
+      },
+      "id": "2kF1XknIpg48",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "i         Labels    IRE       IREth     \n",
+            "0         [1.]      0.59      1.75      \n",
+            "1         [1.]      0.61      1.75      \n",
+            "2         [1.]      0.69      1.75      \n",
+            "3         [1.]      0.71      1.75      \n",
+            "4         [1.]      1.57      1.75      \n",
+            "5         [1.]      0.6       1.75      \n",
+            "6         [1.]      0.68      1.75      \n",
+            "7         [1.]      0.66      1.75      \n",
+            "8         [1.]      0.7       1.75      \n",
+            "9         [1.]      0.62      1.75      \n",
+            "10        [1.]      0.51      1.75      \n",
+            "11        [1.]      0.82      1.75      \n",
+            "12        [1.]      0.75      1.75      \n",
+            "13        [1.]      0.63      1.75      \n",
+            "14        [1.]      0.59      1.75      \n",
+            "15        [1.]      1.13      1.75      \n",
+            "16        [1.]      0.66      1.75      \n",
+            "17        [1.]      1.12      1.75      \n",
+            "18        [1.]      0.57      1.75      \n",
+            "19        [1.]      0.78      1.75      \n",
+            "20        [1.]      0.57      1.75      \n",
+            "21        [1.]      0.29      1.75      \n",
+            "22        [1.]      0.48      1.75      \n",
+            "23        [1.]      0.99      1.75      \n",
+            "24        [1.]      0.66      1.75      \n",
+            "25        [1.]      0.86      1.75      \n",
+            "26        [1.]      0.59      1.75      \n",
+            "27        [1.]      0.77      1.75      \n",
+            "28        [1.]      0.69      1.75      \n",
+            "29        [0.]      0.45      1.75      \n",
+            "30        [1.]      0.4       1.75      \n",
+            "31        [1.]      0.87      1.75      \n",
+            "32        [1.]      0.54      1.75      \n",
+            "33        [1.]      0.29      1.75      \n",
+            "34        [1.]      0.93      1.75      \n",
+            "35        [1.]      0.3       1.75      \n",
+            "36        [1.]      0.17      1.75      \n",
+            "37        [1.]      0.74      1.75      \n",
+            "38        [0.]      0.28      1.75      \n",
+            "39        [0.]      0.34      1.75      \n",
+            "40        [0.]      0.35      1.75      \n",
+            "41        [0.]      0.5       1.75      \n",
+            "42        [1.]      0.82      1.75      \n",
+            "43        [1.]      0.36      1.75      \n",
+            "44        [1.]      1.12      1.75      \n",
+            "45        [1.]      0.34      1.75      \n",
+            "46        [1.]      0.34      1.75      \n",
+            "47        [1.]      0.75      1.75      \n",
+            "48        [1.]      0.56      1.75      \n",
+            "49        [1.]      0.37      1.75      \n",
+            "50        [1.]      0.3       1.75      \n",
+            "51        [1.]      0.3       1.75      \n",
+            "52        [1.]      0.65      1.75      \n",
+            "53        [1.]      0.5       1.75      \n",
+            "54        [0.]      0.21      1.75      \n",
+            "55        [1.]      1.06      1.75      \n",
+            "56        [1.]      0.4       1.75      \n",
+            "57        [1.]      0.6       1.75      \n",
+            "58        [1.]      0.6       1.75      \n",
+            "59        [1.]      0.67      1.75      \n",
+            "60        [1.]      0.55      1.75      \n",
+            "61        [0.]      0.24      1.75      \n",
+            "62        [0.]      0.64      1.75      \n",
+            "63        [0.]      1.75      1.75      \n",
+            "64        [0.]      0.89      1.75      \n",
+            "65        [0.]      0.38      1.75      \n",
+            "66        [1.]      0.56      1.75      \n",
+            "67        [1.]      0.73      1.75      \n",
+            "68        [1.]      0.49      1.75      \n",
+            "69        [1.]      0.47      1.75      \n",
+            "70        [1.]      0.26      1.75      \n",
+            "71        [1.]      0.51      1.75      \n",
+            "72        [1.]      0.83      1.75      \n",
+            "73        [1.]      0.71      1.75      \n",
+            "74        [0.]      0.4       1.75      \n",
+            "75        [0.]      1.28      1.75      \n",
+            "76        [1.]      0.42      1.75      \n",
+            "77        [1.]      1.34      1.75      \n",
+            "78        [1.]      0.55      1.75      \n",
+            "79        [1.]      0.92      1.75      \n",
+            "80        [1.]      0.65      1.75      \n",
+            "81        [1.]      0.82      1.75      \n",
+            "82        [1.]      1.23      1.75      \n",
+            "83        [1.]      0.4       1.75      \n",
+            "84        [1.]      0.49      1.75      \n",
+            "85        [1.]      0.47      1.75      \n",
+            "86        [1.]      0.45      1.75      \n",
+            "87        [1.]      0.69      1.75      \n",
+            "88        [1.]      0.58      1.75      \n",
+            "89        [1.]      0.76      1.75      \n",
+            "90        [1.]      0.57      1.75      \n",
+            "91        [1.]      0.33      1.75      \n",
+            "92        [1.]      0.57      1.75      \n",
+            "93        [1.]      0.48      1.75      \n",
+            "94        [1.]      0.31      1.75      \n",
+            "95        [1.]      0.42      1.75      \n",
+            "96        [1.]      0.43      1.75      \n",
+            "97        [1.]      0.51      1.75      \n",
+            "98        [1.]      0.46      1.75      \n",
+            "99        [1.]      0.43      1.75      \n",
+            "100       [1.]      0.54      1.75      \n",
+            "101       [1.]      0.63      1.75      \n",
+            "102       [1.]      0.47      1.75      \n",
+            "103       [1.]      0.51      1.75      \n",
+            "104       [1.]      0.49      1.75      \n",
+            "105       [1.]      0.25      1.75      \n",
+            "106       [1.]      0.16      1.75      \n",
+            "107       [1.]      0.2       1.75      \n",
+            "108       [1.]      0.7       1.75      \n",
+            "Обнаружено  96.0  аномалий\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Для AE3_v1 точность составляет 88%"
+      ],
+      "metadata": {
+        "id": "LXHt7-gxuJIy"
+      },
+      "id": "LXHt7-gxuJIy"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# тестирование АE2\n",
+        "lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5ow1D88nsrir",
+        "outputId": "cc278276-2b8e-4ae0-b723-81718aed5542"
+      },
+      "id": "5ow1D88nsrir",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "i         Labels    IRE       IREth     \n",
+            "0         [1.]      0.69      1.6       \n",
+            "1         [1.]      0.81      1.6       \n",
+            "2         [1.]      0.51      1.6       \n",
+            "3         [1.]      0.62      1.6       \n",
+            "4         [1.]      1.6       1.6       \n",
+            "5         [1.]      0.34      1.6       \n",
+            "6         [1.]      0.51      1.6       \n",
+            "7         [1.]      0.49      1.6       \n",
+            "8         [1.]      0.5       1.6       \n",
+            "9         [1.]      0.45      1.6       \n",
+            "10        [1.]      0.4       1.6       \n",
+            "11        [1.]      0.3       1.6       \n",
+            "12        [1.]      0.7       1.6       \n",
+            "13        [1.]      0.54      1.6       \n",
+            "14        [1.]      0.59      1.6       \n",
+            "15        [1.]      0.64      1.6       \n",
+            "16        [1.]      0.4       1.6       \n",
+            "17        [1.]      0.73      1.6       \n",
+            "18        [1.]      0.86      1.6       \n",
+            "19        [1.]      0.45      1.6       \n",
+            "20        [1.]      0.71      1.6       \n",
+            "21        [1.]      0.33      1.6       \n",
+            "22        [1.]      0.49      1.6       \n",
+            "23        [1.]      0.83      1.6       \n",
+            "24        [1.]      0.65      1.6       \n",
+            "25        [1.]      0.92      1.6       \n",
+            "26        [1.]      0.41      1.6       \n",
+            "27        [1.]      0.48      1.6       \n",
+            "28        [1.]      0.51      1.6       \n",
+            "29        [0.]      0.42      1.6       \n",
+            "30        [1.]      0.35      1.6       \n",
+            "31        [1.]      0.4       1.6       \n",
+            "32        [1.]      0.42      1.6       \n",
+            "33        [1.]      0.29      1.6       \n",
+            "34        [1.]      0.48      1.6       \n",
+            "35        [1.]      0.28      1.6       \n",
+            "36        [1.]      0.14      1.6       \n",
+            "37        [1.]      0.55      1.6       \n",
+            "38        [0.]      0.26      1.6       \n",
+            "39        [0.]      0.32      1.6       \n",
+            "40        [0.]      0.23      1.6       \n",
+            "41        [0.]      0.43      1.6       \n",
+            "42        [1.]      0.46      1.6       \n",
+            "43        [1.]      0.26      1.6       \n",
+            "44        [1.]      0.92      1.6       \n",
+            "45        [1.]      0.34      1.6       \n",
+            "46        [1.]      0.34      1.6       \n",
+            "47        [1.]      0.69      1.6       \n",
+            "48        [0.]      0.49      1.6       \n",
+            "49        [1.]      0.44      1.6       \n",
+            "50        [1.]      0.31      1.6       \n",
+            "51        [1.]      0.54      1.6       \n",
+            "52        [1.]      0.66      1.6       \n",
+            "53        [1.]      0.45      1.6       \n",
+            "54        [0.]      0.26      1.6       \n",
+            "55        [1.]      0.44      1.6       \n",
+            "56        [1.]      0.45      1.6       \n",
+            "57        [1.]      0.48      1.6       \n",
+            "58        [1.]      0.68      1.6       \n",
+            "59        [1.]      0.75      1.6       \n",
+            "60        [1.]      0.5       1.6       \n",
+            "61        [0.]      0.34      1.6       \n",
+            "62        [1.]      0.33      1.6       \n",
+            "63        [1.]      0.54      1.6       \n",
+            "64        [1.]      0.57      1.6       \n",
+            "65        [1.]      0.36      1.6       \n",
+            "66        [1.]      0.48      1.6       \n",
+            "67        [1.]      0.66      1.6       \n",
+            "68        [1.]      0.36      1.6       \n",
+            "69        [1.]      0.53      1.6       \n",
+            "70        [1.]      0.25      1.6       \n",
+            "71        [1.]      0.58      1.6       \n",
+            "72        [1.]      0.47      1.6       \n",
+            "73        [1.]      0.45      1.6       \n",
+            "74        [1.]      0.46      1.6       \n",
+            "75        [1.]      1.04      1.6       \n",
+            "76        [1.]      0.25      1.6       \n",
+            "77        [1.]      0.99      1.6       \n",
+            "78        [1.]      0.37      1.6       \n",
+            "79        [1.]      0.52      1.6       \n",
+            "80        [1.]      0.39      1.6       \n",
+            "81        [1.]      0.52      1.6       \n",
+            "82        [1.]      0.59      1.6       \n",
+            "83        [1.]      0.38      1.6       \n",
+            "84        [1.]      0.44      1.6       \n",
+            "85        [1.]      0.45      1.6       \n",
+            "86        [1.]      0.41      1.6       \n",
+            "87        [1.]      0.55      1.6       \n",
+            "88        [1.]      0.61      1.6       \n",
+            "89        [1.]      0.75      1.6       \n",
+            "90        [1.]      0.4       1.6       \n",
+            "91        [1.]      0.23      1.6       \n",
+            "92        [1.]      0.36      1.6       \n",
+            "93        [1.]      0.39      1.6       \n",
+            "94        [1.]      0.22      1.6       \n",
+            "95        [1.]      0.43      1.6       \n",
+            "96        [1.]      0.44      1.6       \n",
+            "97        [1.]      0.43      1.6       \n",
+            "98        [1.]      0.41      1.6       \n",
+            "99        [1.]      0.54      1.6       \n",
+            "100       [1.]      0.48      1.6       \n",
+            "101       [1.]      0.45      1.6       \n",
+            "102       [1.]      0.34      1.6       \n",
+            "103       [1.]      0.4       1.6       \n",
+            "104       [1.]      0.46      1.6       \n",
+            "105       [1.]      0.33      1.6       \n",
+            "106       [1.]      0.19      1.6       \n",
+            "107       [1.]      0.21      1.6       \n",
+            "108       [1.]      0.62      1.6       \n",
+            "Обнаружено  101.0  аномалий\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Для AE3_v2 точность составляет 92%"
+      ],
+      "metadata": {
+        "id": "LDsC5WyeuRHS"
+      },
+      "id": "LDsC5WyeuRHS"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 9) Если результаты обнаружения аномалий не удовлетворительные (обнаружено менее 70% аномалий), то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (9)."
+      ],
+      "metadata": {
+        "id": "fPPbYwnQpqS3"
+      },
+      "id": "fPPbYwnQpqS3"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Результаты обнаружения аномалий удовлетворены."
+      ],
+      "metadata": {
+        "id": "IVmw2aeduXml"
+      },
+      "id": "IVmw2aeduXml"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 10) Параметры наилучшего автокодировщика и результаты обнаружения аномалий занести в таблицу:\n",
+        "Табл. 2 Результаты задания №2"
+      ],
+      "metadata": {
+        "id": "u3cAX_IgpvWU"
+      },
+      "id": "u3cAX_IgpvWU"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "| Dataset name | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | % обнаруженных<br>аномалий |\n",
+        "|:-------------|:-----------------------------|:----------------------------------------|:-----------------------------|:-------------------|:-------------------------------|:---------------------------|\n",
+        "| Cardio       | 11                             | 48, 36, 28, 22, 16, 10, 16, 22, 28, 36, 48              | 100000                      | 0.0098              | 1.6                           | 92%                      |\n"
+      ],
+      "metadata": {
+        "id": "tryfJdjTvgU8"
+      },
+      "id": "tryfJdjTvgU8"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 11) Сделать выводы о требованиях к:\n",
+        "- данным для обучения,\n",
+        "- архитектуре автокодировщика,  \n",
+        "- количеству эпох обучения,\n",
+        "- ошибке MSE_stop, приемлемой для останова обучения,\n",
+        "- ошибке реконструкции обучающей выборки (порогу обнаружения\n",
+        "аномалий)\n",
+        "\n",
+        "для качественного обнаружения аномалий в случае, когда размерность\n",
+        "пространства признаков высока."
+      ],
+      "metadata": {
+        "id": "eE7IYyGJp0Vv"
+      },
+      "id": "eE7IYyGJp0Vv"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1) Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение\n",
+        "2) Архитектура автокодировщика должна постепенно сужатся к бутылочному горлышку,а затем постепенно возвращатся к исходным выходным размерам, кол-во скрытых слоев 7-11.\n",
+        "3) В рамках данного набора данных оптимальное кол-во эпох 100000 с patience 4000 эпох\n",
+        "4) Оптимальная ошибка MSE-stop в районе 0.001, желательно не меньше для предотвращения переобучения\n",
+        "5) Значение порога не больше 1.6"
+      ],
+      "metadata": {
+        "id": "67p7AeSWwVXE"
+      },
+      "id": "67p7AeSWwVXE"
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/labworks/LW2/out/AE1.h5 b/labworks/LW2/out/AE1.h5
new file mode 100644
index 0000000..240b18c
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diff --git a/labworks/LW2/out/AE1_ire_th.txt b/labworks/LW2/out/AE1_ire_th.txt
new file mode 100644
index 0000000..9f0db73
--- /dev/null
+++ b/labworks/LW2/out/AE1_ire_th.txt
@@ -0,0 +1 @@
+5.95
\ No newline at end of file
diff --git a/labworks/LW2/out/AE2.h5 b/labworks/LW2/out/AE2.h5
new file mode 100644
index 0000000..4f40c46
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diff --git a/labworks/LW2/out/AE2_ire_th.txt b/labworks/LW2/out/AE2_ire_th.txt
new file mode 100644
index 0000000..1615779
--- /dev/null
+++ b/labworks/LW2/out/AE2_ire_th.txt
@@ -0,0 +1 @@
+0.45
\ No newline at end of file
diff --git a/labworks/LW2/out/IRE_trainingAE1.png b/labworks/LW2/out/IRE_trainingAE1.png
new file mode 100644
index 0000000..637e06a
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diff --git a/labworks/LW2/out/IRE_trainingAE2.png b/labworks/LW2/out/IRE_trainingAE2.png
new file mode 100644
index 0000000..3cb28b0
Binary files /dev/null and b/labworks/LW2/out/IRE_trainingAE2.png differ
diff --git a/labworks/LW2/out/train_set.png b/labworks/LW2/out/train_set.png
new file mode 100644
index 0000000..f9738e3
Binary files /dev/null and b/labworks/LW2/out/train_set.png differ
diff --git a/labworks/LW2/report.md b/labworks/LW2/report.md
new file mode 100644
index 0000000..f004e94
--- /dev/null
+++ b/labworks/LW2/report.md
@@ -0,0 +1,313 @@
+# Отчёт по лабораторной работе №2
+
+**Кнзев Станислав, Жихарев Данила — А-02-22**
+
+---
+## Задание 1
+
+### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули.
+
+```python
+# импорт модулей
+import os
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')
+
+import numpy as np
+import lab02_lib as lib
+```
+
+### 2) Сгенерировали индивидуальный набор двумерных данных в пространстве признаков с координатами центра (6, 6), где 6 – номер бригады. Вывели полученные данные на рисунок и в консоль.
+
+```python
+# генерация датасета
+data = lib.datagen(6, 6, 1000, 2)
+
+# вывод данных и размерности
+print('Исходные данные:')
+print(data)
+print('Размерность данных:')
+print(data.shape)
+```
+
+
+### 3) Создали и обучили автокодировщик AE1 простой архитектуры, выбрав небольшое количество эпох обучения. Зафиксировали в таблице вида табл.1 количество скрытых слоёв и нейронов в них
+
+```python
+# обучение AE1
+patience = 300
+ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt',
+1000, True, patience)
+```
+
+### 4) Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий.
+
+Ошибка MSE_AE1 = 19.5568
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE1, IREth1, 'AE1')
+```
+
+### 5) Создали и обучили второй автокодировщик AE2 с усложненной архитектурой, задав большее количество эпох обучения
+
+```python
+# обучение AE2
+ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt',
+3000, True, patience)
+```
+
+### 6) Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали второй порог ошибки реконструкции – порог обнаружения аномалий.
+
+Ошибка MSE_AE2 = 0.0108
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE2, IREth2, 'AE2')
+```
+
+### 7) Рассчитали характеристики качества обучения EDCA для AE1 и AE2. Визуализировали и сравнили области пространства признаков, распознаваемые автокодировщиками AE1 и AE2. Сделали вывод о пригодности AE1 и AE2 для качественного обнаружения аномалий.
+
+```python
+# построение областей покрытия и границ классов
+# расчет характеристик качества обучения
+numb_square = 20
+xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
+```
+
+```python
+# построение областей покрытия и границ классов
+# расчет характеристик качества обучения
+numb_square = 20
+xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
+```
+
+```python
+# сравнение характеристик качества обучения и областей аппроксимации
+lib.plot2in1(data, xx, yy, Z1, Z2)
+```
+
+### 8) Если автокодировщик AE2 недостаточно точно аппроксимирует область обучающих данных, то подобрать подходящие параметры автокодировщика и повторить шаги (6) – (8).
+
+Полученные показатели EDCA для автокодировщика AE2 нас устраивают.
+
+### 9) Изучили сохраненный набор данных и пространство признаков. Создали тестовую выборку, состоящую, как минимум, из 4ёх элементов, не входящих в обучающую выборку. Элементы должны быть такими, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии.
+
+```python
+# загрузка тестового набора
+data_test = np.loadtxt('data_test.txt', dtype=float)
+print(data_test)
+```
+
+### 10) Применили обученные автокодировщики AE1 и AE2 к тестовым данным и вывели значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога на график и в консоль.
+
+```python
+# тестирование АE1
+predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)
+```
+
+```python
+# тестирование АE1
+lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)
+lib.ire_plot('test', ire1, IREth1, 'AE1')
+```
+
+```python
+# тестирование АE2
+predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)
+```
+
+```python
+# тестирование АE2
+lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)
+lib.ire_plot('test', ire2, IREth2, 'AE2')
+```
+
+### 11) Визуализировали элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2.
+
+```python
+# построение областей аппроксимации и точек тестового набора
+lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
+```
+
+### 12) Результаты исследования занесли в таблицу:
+Табл. 1 Результаты задания №1
+
+|      | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | Значение показателя<br>Excess | Значение показателя<br>Approx | Количество обнаруженных<br>аномалий |
+|-----:|------------------------------|----------------------------------------|-----------------------------|--------------------|-------------------------------|-------------------------------|--------------------------------|-------------------------------------|
+| AE1  | 1                            | 1                            | 1000                        | 19.5568             | 6.53                         | 12.67                          | 0.073                           | 0                                   |
+| AE2  | 5                            | 3,2,1,2,3                                | 3000                          | 0.0108             | 0.4                         | 0.33                          | 0.750                           | 4                                   |
+
+### 13) Сделали выводы о требованиях к:
+- данным для обучения,
+- архитектуре автокодировщика,
+- количеству эпох обучения,
+- ошибке MSE_stop, приемлемой для останова обучения,
+- ошибке реконструкции обучающей выборки (порогу обнаружения
+аномалий),
+- характеристикам качества обучения EDCA одноклассового
+классификатора
+
+для качественного обнаружения аномалий в данных.
+
+1) Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение
+2) Архитектура автокодировщика должна постепенно сужатся к бутылочному горлышку,а затем постепенно возвращатся к исходным выходным размерам, кол-во скрытых слоев 3-5
+3) В рамках данного набора данных оптимальное кол-во эпох 3000 с patience 300 эпох
+4) Оптимальная ошибка MSE-stop в районе 0.01, желательно не меньше для предотвращения переобучения
+5) Значение порога в районе 0.4
+6) Значение Excess не больше 0.5, значение Deficit равное 0, значение Coating равное 1, значение Approx не меньше 0.7
+
+## Задание 2
+
+### 1) Изучить описание своего набора реальных данных, что он из себя представляет
+
+Бригада 6 => набор данных Cardio. Это реальный набор данных, который состоит из измерений частоты сердечных сокращений плода и
+сокращений матки на кардиотокограммах, классифицированных экспертами
+акушерами. Исходный набор данных предназначен для классификации. В нем
+представлено 3 класса: «норма», «подозрение» и «патология». Для обнаружения
+аномалий класс «норма» принимается за норму, класс «патология» принимается за
+аномалии, а класс «подозрение» был отброшен.
+
+| Количество<br>признаков | Количество<br>примеров | Количество<br>нормальных примеров | Количество<br>аномальных примеров |
+|-------------------------:|-----------------------:|----------------------------------:|-----------------------------------:|
+| 21                       | 1764                   | 1655                               | 109                                |
+
+### 2) Загрузить многомерную обучающую выборку реальных данных Cardio.txt.
+
+```python
+# загрузка обчуающей выборки
+train = np.loadtxt('data/cardio_train.txt', dtype=float)
+```
+
+### 3) Вывести полученные данные и их размерность в консоли.
+
+```python
+print('train:\n', train)
+print('train.shape:', np.shape(train))
+```
+
+### 4) Создать и обучить автокодировщик с подходящей для данных архитектурой. Выбрать необходимое количество эпох обучения.
+
+```python
+# **kwargs
+# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)
+# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)
+# early_stopping_value = значение для ранней остановки (по умолчанию -  0.0001)
+
+from time import time
+
+patience = 4000
+start = time()
+ae3_v1_trained, IRE3_v1, IREth3_v1 = lib.create_fit_save_ae(train,'out/AE3_V1.h5','out/AE3_v1_ire_th.txt',
+100000, False, patience, early_stopping_delta = 0.001)
+print("Время на обучение: ", time() - start)
+```
+
+### 5) Зафиксировать ошибку MSE, на которой обучение завершилось. Построить график ошибки реконструкции обучающей выборки. Зафиксировать порог ошибки реконструкции – порог обнаружения аномалий.
+
+Скрытых слоев 7, нейроны: 46->26->14->10->14->26->48
+
+Ошибка MSE_AE3_v1 = 0.0126
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE3_v1, IREth3_v1, 'AE3_v1')
+```
+
+### 6) Сделать вывод о пригодности обученного автокодировщика для качественного обнаружения аномалий. Если порог ошибки реконструкции слишком велик, то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (6).
+
+```python
+# **kwargs
+# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)
+# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)
+# early_stopping_value = значение для ранней остановки (по умолчанию -  0.0001)
+
+from time import time
+
+patience = 4000
+start = time()
+ae3_v2_trained, IRE3_v2, IREth3_v2 = lib.create_fit_save_ae(train,'out/AE3_V2.h5','out/AE3_v2_ire_th.txt',
+100000, False, patience, early_stopping_delta = 0.001)
+print("Время на обучение: ", time() - start)
+```
+
+Скрытых слоев 7, нейроны: 48->36->28->22->16->12->16->22->28->36->48
+
+Ошибка MSE_AE3_v1 = 0.0098
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE3_v2, IREth3_v2, 'AE3_v2')
+```
+
+### 7) Изучить и загрузить тестовую выборку Cardio.txt.
+
+```python
+#загрузка тестовой выборки
+test = np.loadtxt('data/cardio_test.txt', dtype=float)
+print('\n test:\n', test)
+print('test.shape:', np.shape(test))
+```
+
+### 8) Подать тестовую выборку на вход обученного автокодировщика для обнаружения аномалий. Вывести график ошибки реконструкции элементов тестовой выборки относительно порога.
+
+```python
+# тестирование АE3
+predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1)
+```
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')
+```
+
+```python
+# тестирование АE3
+predicted_labels3_v2, ire3_v2 = lib.predict_ae(ae3_v2_trained, test, IREth3_v2)
+```
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')
+```
+
+```python
+# тестирование АE2
+lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)
+```
+
+Для AE3_v1 точность составляет 88%
+
+```python
+# тестирование АE2
+lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)
+```
+
+Для AE3_v2 точность составляет 92%
+
+### 9) Если результаты обнаружения аномалий не удовлетворительные (обнаружено менее 70% аномалий), то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (9).
+
+Результаты обнаружения аномалий удовлетворены.
+
+### 10) Параметры наилучшего автокодировщика и результаты обнаружения аномалий занести в таблицу:
+Табл. 2 Результаты задания №2
+
+| Dataset name | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | % обнаруженных<br>аномалий |
+|:-------------|:-----------------------------|:----------------------------------------|:-----------------------------|:-------------------|:-------------------------------|:---------------------------|
+| Cardio       | 11                             | 48, 36, 28, 22, 16, 10, 16, 22, 28, 36, 48              | 100000                      | 0.0098              | 1.6                           | 92%                      |
+
+### 11) Сделать выводы о требованиях к:
+- данным для обучения,
+- архитектуре автокодировщика,  
+- количеству эпох обучения,
+- ошибке MSE_stop, приемлемой для останова обучения,
+- ошибке реконструкции обучающей выборки (порогу обнаружения
+аномалий)
+
+для качественного обнаружения аномалий в случае, когда размерность
+пространства признаков высока.
+
+1) Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение
+2) Архитектура автокодировщика должна постепенно сужатся к бутылочному горлышку,а затем постепенно возвращатся к исходным выходным размерам, кол-во скрытых слоев 7-11.
+3) В рамках данного набора данных оптимальное кол-во эпох 100000 с patience 4000 эпох
+4) Оптимальная ошибка MSE-stop в районе 0.001, желательно не меньше для предотвращения переобучения
+5) Значение порога не больше 1.6